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https://github.com/MachineLearningSystem/Magicube

Magicube is a high-performance library for quantized sparse matrix operations (SpMM and SDDMM) of deep learning on Tensor Cores.
https://github.com/MachineLearningSystem/Magicube

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Magicube is a high-performance library for quantized sparse matrix operations (SpMM and SDDMM) of deep learning on Tensor Cores.

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## Magicube: Efficient Quantized Sparse Matrix Operations on Tensor Cores



![Magicube Logo](magicubeLogo.svg)



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](https://sc22.supercomputing.org/), 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:



```bash

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

```



Run the container and activate python by:



```bash

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:



```bash

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:



```bash

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:



```bash

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:



```bash

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:



```bash

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:



```bash

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.



```bash

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:



```bash

wget https://storage.googleapis.com/sgk-sc2020/dlmc.tar.gz

tar -xvf dlmc.tar.gz

export dataset_dir=/the/path/of/dlmc 

git clone [email protected]:Shigangli/Magicube.git

```



Setup python environment:



```bash

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:

```bibtex

@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](LICENSE).