Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/linghaosong/AccPar
https://github.com/linghaosong/AccPar
Last synced: about 1 month ago
JSON representation
- Host: GitHub
- URL: https://github.com/linghaosong/AccPar
- Owner: linghaosong
- License: mit
- Created: 2021-04-10T07:08:16.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-04-10T07:30:30.000Z (over 3 years ago)
- Last Synced: 2024-02-17T09:34:15.910Z (10 months ago)
- Language: C++
- Size: 14.6 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-Auto-Parallelism - AccPar
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}
}