ConvStencil

ConvStencil: Transform Stencil Computation to Matrix Multiplication on Tensor Cores

Abstract

This artifact contains the source code of ConvStencil, a novel stencil computing system to transform stencil computation to matrix multiplication on Tensor Cores efficiently.

Prerequisites

• Hardware
  • x86-64 CPU
  • a single NVIDIA A100 GPU
• Software (attached in the docker image)
  • CUDA - 12.2 (Tested). Lower versions down to CUDA 11.0 are also supported, but it may affect the performance.
  • GCC - above 9.4.0. You may also try to use icx or clang.
  • cuDNN - above 8.0

Getting Code

The code can be downloaded using git:

git clone https://github.com/microsoft/ConvStencil.git

Compile

Use the following commands:

mkdir -p build
cd build
cmake ..
make all -j24

Usage

You can run convstencil in the following input format.

convstencil_program shape input_size time_interation_size options

• convstencil_program can be chosen from convstencil_1d, convstencil_2d, and convstencil_3d for different dimensions.
• shape can be chosen by the different dimension:
  • 1d1r and 1d2r for 1D
  • star2d1r, box2d1r, star2d3r and box2d3r for 2D
  • star3d1r and box3d1r for 3D
• input_size depends on the number of dimensions; the number of inputs required is equal to the number of dimensions.
• time_interation_size is the iteration time.
• options:
  • --help prints the help information.
  • --custom inputs the custom stencil kernel weights.

Contact

If you have any questions, please send an email to the author at [email protected].

Reference

Yuetao Chen, Kun Li, Yuhao Wang, Donglin Bai, Lei Wang, Lingxiao Ma, Liang Yuan, Yunquan Zhang, Ting Cao, Mao Yang. ConvStencil: Transform Stencil Computation to Matrix Multiplication on Tensor Cores. In ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), pp. 333–347, 2024.

If you use our code, please cite our paper:

@inproceedings{10.1145/3627535.3638476,
author = {Chen, Yuetao and Li, Kun and Wang, Yuhao and Bai, Donglin and Wang, Lei and Ma, Lingxiao and Yuan, Liang and Zhang, Yunquan and Cao, Ting and Yang, Mao},
title = {ConvStencil: Transform Stencil Computation to Matrix Multiplication on Tensor Cores},
year = {2024},
isbn = {9798400704352},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3627535.3638476},
doi = {10.1145/3627535.3638476},
booktitle = {Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming},
pages = {333–347},
series = {PPoPP '24}
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.