Update README.md

README.md

@@ -1,5 +1,5 @@
 # Parallel coroutine operations on Kotlin collections
-Provides parallelized map, ~~reduce~~, ~~etc.~~ operations using coroutines in Kotlin.
+Provides parallelized map, reduce, etc. operations using coroutines in Kotlin.
 
 At this point, there is only a parallel map implementation called .mapParallel(). It is implemented like this.
 ```kotlin
@@ -19,6 +19,17 @@ fun showCase() {
 ```
 If you want to achieve multithreading, make sure to run the coroutine with the Default dispatcher.
 
+## Chunked operations
+Chunked operations improve performance since they split the collection into just a couple of segments,
+which are processed each by a single thread. That benefits from data locality and lesser thread management.
+It is particularly useful (pretty much needed for operations like sum) in the reduce operation when using multithreading,
+since each thread takes one chunk that it reduces on its own. After all threads finish, their results are then reduced again to the final result.
+
+## Benchmarks
+This source includes some benchmarks in the test source folder using [JUnitBenchmarks](http://labs.carrotsearch.com/junit-benchmarks-tutorial.html). Performance of the methods (chunked vs. not chunked and Dispatcher.Default vs Dispatcher.Main) depends on the type of the transformation operation (whether it's blocking for a long time, suspending or really quick).
+
+Example benchmarks will be included here soon.
+
 ## Gradle
 To include this in you gradle project add the following to you root build.gradle file.
 ```gradle
@@ -37,7 +48,4 @@ dependencies {
 ```
 
 ## Future
-In the future, I would like parallel reduce and other transformation functions to be implemented,
-as well as chunked variations of the map and future operations.
-Chunked operations could potentially improve performance since they would split the collection into just a couple of segments,
-which would be processed each by a single thread. That could benefit from data locality and lesser thread management.
+In the future, I would like other transformation functions to be implemented.