https://github.com/MachineLearningSystem/AccPar

https://github.com/MachineLearningSystem/AccPar

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/MachineLearningSystem/AccPar
• Owner: MachineLearningSystem
• License: mit
• Fork: true (linghaosong/AccPar)
• Created: 2022-08-23T01:05:58.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2021-04-10T07:30:30.000Z (over 3 years ago)
• Last Synced: 2024-08-02T19:36:46.324Z (5 months ago)
• Size: 14.6 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-AI-system - Accpar: Tensor partitioning for heterogeneous deep learning accelerators HPCA'20

README

        
# AccPar

Partition tensors in layers for mutiple accelerators.

To compile:

    make

To print partitioning results:

    ./accpar ./networks/Alexnet.txt -1

    

If you find this code useful in your research, please cite:

    @inproceedings{song2020accpar,

    title={Accpar: Tensor partitioning for heterogeneous deep learning accelerators},

    author={Song, Linghao and Chen, Fan and Zhuo, Youwei and Qian, Xuehai and Li, Hai and Chen, Yiran},

    booktitle={2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)},

    pages={342--355},

    year={2020},

    organization={IEEE}

    }

    

    @inproceedings{song2019hypar,

    title={Hypar: Towards hybrid parallelism for deep learning accelerator array},

    author={Song, Linghao and Mao, Jiachen and Zhuo, Youwei and Qian, Xuehai and Li, Hai and Chen, Yiran},

    booktitle={2019 IEEE International Symposium on High Performance Computer Architecture (HPCA)},

    pages={56--68},

    year={2019},

    organization={IEEE}

    }