AshPipe: Asynchronous Hybrid Pipeline Parallel for DNN Training

Author:

Hosoki Ryubu1ORCID,Endo Toshio1ORCID,Hirofuchi Takahiro2ORCID,Ikegami Tsutomu2ORCID

Affiliation:

1. Tokyo Institute of Technology, Japan

2. National Institute of Advanced Industrial Science and Technology, Japan

Funder

Japan Society for the Promotion of Science

Publisher

ACM

Reference31 articles.

1. HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training with TensorFlow

2. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.

3. Chi-Chung Chen, Chia-Lin Yang, and Hsiang-Yun Cheng. 2018. Efficient and robust parallel DNN training through model parallelism on multi-GPU platform. arXiv preprint arXiv:1809.02839 (2018).

4. Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. 2016. Training deep nets with sublinear memory cost. arXiv preprint arXiv:1604.06174 (2016).

5. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).

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