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This repository contains the artifact for the SOSP'23 paper:

Sishuai Gong, Dinglan Peng, Deniz Altınbüken, Pedro Fonseca, Petros Maniatis, "Snowcat: Efficient Kernel Concurrency Testing using a Learned Coverage Predictor".

What is Snowcat?

Snowcat is a kernel concurrency testing framework. It differs from existing tools in that Snowcat uses a machine learning model to guide testing. The ML model is trained to predict the kernel code coverage given the concurrency test (i.e., a pair of sequential inputs and a schedule hint). With the predicted coverage, Snowcat can evaluate if the concurrency test is interesting and only execute interesting tests.

Overview of Snowcat implementation

The implementation of Snowcat has two major components---learning and testing.

  • The learning component (code under snowcat/learning/) is used to:
    • train the PIC model.
    • make inference using a trained PIC.
  • The testing component (code under snowcat/tool/) is used to:
    • execute sequential test inputs (STIs) and concurrent tests (CTs).
    • collect training data.

Getting started

Prerequisite

  • Minimum:

    OS: Ubuntu-18.04

    Processor: 32-core CPU

    Memory: 128GB memory

    Graphics: None

    Storage: 2TB disk + Btrfs compression enabled (see how to enable compression in this doc)

  • Recommended:

    OS: Ubuntu-18.04

    Processor: 96-core CPU

    Memory: 680 memory

    Graphics: 8 Nvidia A100 (40GB) GPUs

    Storage: 20TB disk + Btrfs compression enabled (see how to enable compression in this doc)

The following instructions assume a minimum and CPU-only hardware spec.

Get Snowcat ready (ETA: 30 minutes)

  • The testing component requires some compilation and it can be done with the following commands:

    $ cd $ARTIFACT_HOME # the path where Snowcat is downloaded/cloned.
$ cd script
# source testing_setup.sh $SNOWCAT_STORAGE
$ source testing_setup.sh snowcat/storage # the path to store all outputs.

    Note: the above script exports several important ENV variables that are cruical for Snowcat. Please ensure the script is always sourced in your current bash session.

  • The learning component requires installing some ML liraries on a CPU-only machine. Please (1) manually install anaconda and (2) run the following commands:

    $ cd $ARTIFACT_HOME # the path where Snowcat is downloaded/cloned.
$ cd script
$ ./learning_setup_cpu.sh

    Note: A new Conda environment snowcat-cpu is created after running the above commands. Before moving next, it is recommended to exit the current bash session, start a new one and make sure the environment snowcat-cpu is activated. Also, please source testing_setup.sh again 😉.

Step-1: Collect training data

Collect the execution data of sequential test inputs (ETA: 15 minutes)

Some execution information about the sequential test inputs (e.g., control flow) is needed for Snowcat to generate the input graph to the model.

How to run? This repo provides a coprus of sequential test inputs and they can be analyzed by Snowcat with the following commands:

# make sure script/testing_setup.sh is sourced.
$ cd $ARTIFACT_HOME # the path where Snowcat is downloaded/cloned.
$ cd script/data-collection/sti-data
# $ ./collect_sti_data.sh $STI_ID_A $STI_ID_B
$ ./collect_sti_data.sh 100 150

$STI_ID_A and $STI_ID_B together decide a list of sequential test inputs that will be analyzed. In the above case, 50 sequential test inputs (STI-ID==100, STI-ID=101, STI-ID=102, ..., STI-ID=150) will be executed and profiled.

What output is expected?

A folder sti-data under $SNOWCAT_STORAGE will be created.

# make sure script/testing_setup.sh is sourced.
$ cd $ARTIFACT_HOME # the path where Snowcat is downloaded/cloned.
$ ls $SNOWCAT_STORAGE/sti-data
block_assembly_dict  code_block_sequence  error  generator-log  intra_data_flow  raw  sc_control_flow  shared_mem_access  stat  ur_control_flow

Collect the execution data of concurrent test inputs (ETA: 90 minutes)

The coverage of some concurrent test inputs is needed to build a training dataset so that Snowcat can learn from it.

How to run?

Running the following commands will collect the coverage of some random concurrent tests:

# make sure script/testing_setup.sh is sourced.
$ cd $ARTIFACT_HOME # the path where Snowcat is downloaded/cloned.
$ cd script/data-collection/cti-data
# $ ./collect_random_cti_data.sh $number_of_ctis_to_profile; ./extract_coverage.py
$ ./collect_random_cti_data.sh 50; ./extract_coverage.py  # collect the coverage of 50 concurrent test inputs under different interleavings

What output is expected?

A folder cti-data under $SNOWCAT_STORAGE will be created. Subfolder dataset is the dataset we built and raw is the place where raw execution traces are stored.

$ cd $ARTIFACT_HOME # the path where Snowcat is downloaded/cloned.
$ ls $SNOWCAT_STORAGE/cti-data
dataset  raw

Step-2: Train a PIC model

💡For users who want to use GPUs (A100) for training:

Please modify the file $ARTIFACT_HOME/learning/train-config-template.ini and $ARTIFACT_HOME/learning/predict-config-template.ini by changing use_cpu=True to use_cpu=False before continue.

Split the dataset (ETA: 5 minutes)

How to run?

To split the dataset ($SNOWCAT_STORAGE/cti-data/, collected in the last step), one can run:

# make sure script/testing_setup.sh is sourced.
# make sure snowcat-cpu is activated.
$ cd $ARTIFACT_HOME # the path where Snowcat is downloaded/cloned.
$ cd learning/
$ python split_dataset.py

What output is expected?

The script will tell the user where to find the dataset split and, more importantly, config files that can help you start training and inference immediately:

the new dataset split is stored in ./dataset_split/split-2023-07-27-21-29-54
the new training config is stored in ./train-config-2023-07-27-21-29-54.ini
the new inference/predict config is stored in ./predict-config-2023-07-27-21-29-54.ini

Note some warning/error messages might show up to the screen but they are generally acceptable. First, warnings that complain the missing of tensorboardX module is fine since it is not used by us anyway. Second, sometimes we might fail to generate a graph if certain information is missing (e.g., sequential control flow) and this issue occasionally happens when the random sequential test input is ill-defined.

Start training (ETA: 10 hours (5 hours per epoch))

How to run?

Copy the path of the training config and run the following commands:

# make sure script/testing_setup.sh is sourced.
# make sure snowcat-cpu is activated.
$ cd $ARTIFACT_HOME # the path where Snowcat is downloaded/cloned.
$ cd learning/
$ python train.py ./train-config-2023-07-27-20-54-32.ini

What output is expected?

  1. In the middle of each epoch, the current training or validation performance (average precision) is printed periocally.

    2023-07-27 23:31:52.923280 epoch: 01 loss: 0.07475609332323074 rank: 00 update_frequency: 2 trained_graphs_this_gpu: 100 total_graphs_per_gpu: 1238 train_ap_on_all: 0.9034862518310547 train_ap_on_ur: 0.01715177111327648 last_lr_rate: [5e-05]
2023-07-28 01:03:21.298420 epoch: 01 loss: 0.04783577099442482 rank: 01 update_frequency: 2 trained_graphs_this_gpu: 200 total_graphs_per_gpu: 1238 train_ap_on_all: 0.9459865093231201 train_ap_on_ur: 0.013260450214147568 last_lr_rate: [5e-05]

  2. After each epoch, a model checkpoint is created and saved to the disk. One can find them in a folder under $SNOWCAT_STORAGE/training/. The folder is named as train-{timestamp} in which the timestamp is created at the beginning of the training.

    # make sure script/testing_setup.sh is sourced.
$ cd $ARTIFACT_HOME # the path where Snowcat is downloaded/cloned.
$ ls $SNOWCAT_STORAGE/training
train-2023-07-27-21-38-47/
$ ls $SNOWCAT_STORAGE/training/train-2023-07-27-21-38-47/
amp-checkpoint-0.tar  amp-checkpoint-1.tar  backup  bert-parameters  dataset-report  model-arch
# amp-checkpoint-1.tar is the checkpoint made after the first epoch

Step-3: Use the PIC model to make inference (ETA: 30 minutes)

How to run?

Once the model is trained, we can load the checkpoint and make inference on the test dataset. To start inference, one needs to:

  1. Copy the path of the inference/predict config filepath, which is generated in the previous step: Split the dataset.
  2. Copy the path of the model checkpoint, which can be found under the folder $SNOWCAT_STORAGE/training/, which is explained in the previous step.

Then, running the following commands will make inference on the test dataset:

# make sure script/testing_setup.sh is sourced.
# make sure snowcat-cpu is activated.
$ cd $ARTIFACT_HOME # the path where Snowcat is downloaded/cloned.
$ cd learning/
$ python predict.py ./predict-config-2023-07-27-21-29-54.ini $SNOWCAT_STORAGE/training/train-2023-07-27-21-38-47/amp-checkpoint-1.tar

What output is expected?

A folder under $SNOWCAT_STORAGE/inference/ will be created and is named as inference-{timestamp}.

# make sure script/testing_setup.sh is sourced.
$ cd $ARTIFACT_HOME # the path where Snowcat is downloaded/cloned.
$ ls $SNOWCAT_STORAGE/inference
inference-2023-07-28-1-26-23/

Step-4: Emulate SKI (MLPCT) (ETA: 3 minutes)

Based on the inference results of the test dataset, we can emulate a run of SKI. Concurrent tests (CTs) that were predicted in the last step will be considered by different schedulers such as MLPCT and original PCT. In the end, each scheduler will select a few CTs that it wants to execute. Then, we can access the race coverage (collected when building the dataset) and get the race coverage history achieved by this scheduler.

How to run?

Copy the path of the inference result $SNOWCAT_STORAGE/inference/inference-{timestamp} and run the following commands:

$ cd $ARTIFACT_HOME
$ cd evaluation/
$ python emulate_ski.py `$SNOWCAT_STORAGE/inference/inference-{timestamp}`

What output is expected?

A race coverage history graph race-coverage-history.pdf will be generated and stored under $SNOWCAT_STORAGE/graph/.

$ cd $SNOWCAT_STORAGE/graph
$ ls
race-coverage-history.pdf

Artifacts

Instructions about how to access other artifacts such as trained models can be found in artifact_evaluation.

License

Code of Snowboard and SKI hypervisor (tool/ski and tool/snowboard) in this repository is licensed under the GLPv2 license (tool/ski/src/LICENSE and tool/snowboard/src/LICENSE). The rest of Snowcat implementation is under Apache 2 license (script/LICENSE and learning/LICENSE).