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Magicube: Efficient Quantized Sparse Matrix Operations on Tensor Cores

Magicube is a high-performance library for quantized sparse matrix operations (SpMM and SDDMM) of deep learning on Tensor Cores. Magicube is published in SC 2022, Best Paper Finalist. We conduct all the experiments on NVIDIA A100-SXM4-40GB GPU. The software requirements to reproduce the artfifact are: GCC 8.4.1, CUDA Toolkit 11.4.0, Python 3.8.5, PyTorch 1.9.0 with cuDNN version 8005.

We provide two ways to reproduce the results. The first way is to reproduce the artifact with docker container, in which the software environment is already configured and the input dataset is also included. Note that nvidia-docker must be installed to run the container on GPU. Using docker container enables an easy reproducibility process. The second way is to reproduce the artifact with source code, in which users have to setup the software environment and download the input dataset by themselves following the provided instructions.

Reproduction with container

We run all the experiments on NVIDIA A100-SXM4-40GB GPU. Please double-check the model of GPU by nvidia-smi -L. Note that nvidia-docker must be installed to run the container on GPU. Use the following three steps to reproduce the artifact with docker container.

Step 1: Download and run the container.

Download magicube_container.tar.gz from the DOI by:

wget https://zenodo.org/record/6924338/files/magicube_container.tar.gz

Run the container and activate python by:

docker load -i magicube_container.tar.gz
docker run -it --gpus all magicube_container
source /artifacts/sc22_venv/bin/activate

Step 2: Compile and run the experiments.

(1) To reproduce the results of Fig. 11:

cd /artifacts/Magicube/SpMM/ablation_study

# about 3 minutes
bash compile_jobs.sh

# about 3 minutes
bash spmm_ablation_study.sh > spmm_abl_study.txt

(2) To reproduce the results of Fig. 12:

cd /artifacts/Magicube/SpMM/SpMM

bash setup.sh

# about 5 minutes
bash spmm_pres.sh > spmm_pres.txt

(3) To reproduce the results of Fig. 13:

cd /artifacts/Magicube/SDDMM/ablation_study

bash compile_jobs.sh

# about 5 minutes
python sddmm_ablation_study.py > sddmm_abl_study.txt

(4) To reproduce the results of Fig. 14:

cd /artifacts/Magicube/baselines
bash setup.sh

# about 13 hours
bash run_spmm_baselines.sh

cd /artifacts/Magicube/SpMM/SpMM
bash setup.sh

# about 8 hours
bash run_spmm_magicube.sh

(5) To reproduce the results of Fig. 15:

cd /artifacts/Magicube/baselines
bash setup.sh

# about 8 hours
bash run_sddmm_baselines.sh

cd /artifacts/Magicube/SDDMM/SDDMM
bash setup.sh

# about 5 hours
bash run_sddmm_magicube.sh

(6) To reproduce the results of Fig. 16:

cd /artifacts/Magicube/end2end_eval/ sparse_transformer_baselines/src
bash install.sh
cd ..

# about 0.5 hour
python launch_cudnn_fp16.py > pytorch_n2n.txt

# about 0.8 hour
python launch_vectorSparse.py > vectorSparse_n2n.txt

cd /artifacts/Magicube/end2end_eval/sparse_transformer_magicube/src
bash install.sh
cd ..

# about 2.6 hours
python launch_magicube.py > magicube_n2n.txt

Step 3: Plot the figures.

cd /artifacts/Magicube/plot

# generate csv files
bash gen_csv.sh

# plot figures
bash plot.sh

# copy figures
cd /artifacts/Magicube/plot/figs
scp *.pdf username@hostmachine:/host/path/target

Reproduction with source code

Different from docker container, users have to setup the software environment and download the input dataset by themselves when reproducing from source code.

Step 1: Prepare dataset and code, and setup python environment.

Download input dataset and source code:

wget https://storage.googleapis.com/sgk-sc2020/dlmc.tar.gz
tar -xvf dlmc.tar.gz
export dataset_dir=/the/path/of/dlmc 
git clone git@github.com:Shigangli/Magicube.git

Setup python environment:

conda create --name py38_sc22 python=3.8
conda activate py38_sc22
pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

Steps 2&3: Suppose the source code is in the path of /artifacts/Magicube/. Then, follow the same Steps 2&3 as reproduction with container to reproduce the results and figures.

Publication

Magicube is pulished in SC 2022, Best Paper Finalist. To cite our work:

@inproceedings{li2022efficient,
  title={Efficient Quantized Sparse Matrix Operations on Tensor Cores},
  author={Li, Shigang and Osawa, Kazuki and Hoefler, Torsten},
  booktitle={International Conference for High Performance Computing, Networking, Storage and Analysis (SC'22)},
  year={2022}
}

License

See LICENSE.