Aceso: Efficient Parallel DNN Training through Iterative Bottleneck Alleviation-Reference-Cited by-同舟云学术

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Aceso: Efficient Parallel DNN Training through Iterative Bottleneck Alleviation

Published:2024-04-22 Issue: Volume:4 Page:163-181
ISSN:
Container-title:Proceedings of the Nineteenth European Conference on Computer Systems
language:
Short-container-title:

Author:

Liu Guodong¹^ORCID,Miao Youshan²^ORCID,Lin Zhiqi³^ORCID,Shi Xiaoxiang⁴^ORCID,Maleki Saeed²^ORCID,Yang Fan²^ORCID,Bao Yungang⁵^ORCID,Wang Sa⁵^ORCID

Affiliation:

1. State Key Lab of Processors, Institute of Computing Technology, CAS, Univ. of Chinese Academy of Sciences, and Microsoft Research (Asia)

2. Microsoft Research

3. University of Science and Technology of China

4. Shanghai Jiao Tong University

5. State Key Lab of Processors, Institute of Computing Technology, CAS, Univ. of Chinese Academy of Sciences

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3627703.3629554

Reference52 articles.

1. Varuna

2. Benchmark Analysis of Representative Deep Neural Network Architectures

3. Tom B Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020).

4. Integrating scale out and fault tolerance in stream processing using operator state management

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

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