Gradient Synchronization
Gradient Synchronization is a data parallelism technique that belongs to the class of distributed optimization methods, specifically distinguished by its coordination of gradient updates across multiple processing nodes to ensure model consistency during synchronous training.
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3.2K
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164
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55
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2017–2026
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Li Shen, Yanli Zhao, Rohan Varma et al. · Proceedings of the VLDB Endowment · 2020 · 424 citations
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
Wei Wen, Cong Xu, Feng Yan et al. · arXiv (Cornell University) · 2017 · 422 citations · Full text
TernGrad: ternary gradients to reduce communication in distributed deep learning
Wei Wen, Cong Xu, Feng Yan et al. · arXiv (Cornell University) · 2017 · 396 citations · Full text
A generic communication scheduler for distributed DNN training acceleration
Yanghua Peng, Yibo Zhu, Yangrui Chen et al. · 2019 · 319 citations
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Tsinghua University
Beijing, China
5 h-index
25 publications
486 citations
Meta (Israel)
Tel Aviv, Israel
3 h-index
21 publications
5K citations
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
4 publications
114 citations
ETH Zurich
Zurich, Switzerland
3 h-index
3 publications
73 citations
University of Nevada, Reno
Reno, United States
3 h-index
4 publications
890 citations
University of Illinois Urbana-Champaign
Urbana, United States
2 h-index
2 publications
46 citations
Hewlett-Packard (United States)
Palo Alto, United States
2 h-index
2 publications
818 citations
Amazon (United States)
Seattle, United States
2 h-index
7 publications
119 citations
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Repository · 11 publications · 1.2K citations
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Conference · 3 publications · 51 citations
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PVLDB · 2 publications · 441 citations
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1 publications · 10 citations
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1 publications · 20 citations
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