ASHL: An Adaptive Multi-Stage Distributed Deep Learning Training Scheme for Heterogeneous Environments-Reference-Cited by-同舟云学术

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ASHL: An Adaptive Multi-Stage Distributed Deep Learning Training Scheme for Heterogeneous Environments

Published:2024-01 Issue:1 Volume:73 Page:30-43
ISSN:0018-9340
Container-title:IEEE Transactions on Computers
language:
Short-container-title:IEEE Trans. Comput.

Author:

Shen Zhaoyan¹^ORCID,Tang Qingxiang¹^ORCID,Zhou Tianren¹^ORCID,Zhang Yuhao²^ORCID,Jia Zhiping¹^ORCID,Yu Dongxiao¹^ORCID,Zhang Zhiyong³^ORCID,Li Bingzhe⁴^ORCID

Affiliation:

1. School of Computer Science and Technology, Shandong University, Qingdao, China

2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

3. Quan Cheng Laboratory, Jinan, China

4. Computer Science Department, University of Texas at Dallas, Richardson, TX, USA

Funder

Natural Science Foundation of Shandong Province

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/12/10372122/10256683.pdf?arnumber=10256683

Reference44 articles.

1. Parameter server for distributed machine learning;Li,2013

2. Optimizing network performance in distributed machine learning;Mai,2015

3. Gaia: Geo-Distributed machine learning approaching LAN speeds;Hsieh,2017

4. Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce

5. Optimization of Collective Reduction Operations

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