Efficient Partial Reduce Across Clouds
Authors: 
Renyi Wang, Shouxi Luo, Ke Li, Huanlai XingAuthors Info & Claims
Pages 99 - 100
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No abstract available.
References
[1]
Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R. Ganger, Phillip B. Gibbons, and Onur Mutlu. 2017. Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds. In 14th NSDI. USENIX Association, 629–647.
[2]
Shigang Li, Tal Ben-Nun, Salvatore Di Girolamo, Dan Alistarh, and Torsten Hoefler. 2020. Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations. In 25th PPoPP. ACM, 45–61.
[3]
Xupeng Miao, Xiaonan Nie, Yingxia Shao, Zhi Yang, Jiawei Jiang, Lingxiao Ma, and Bin Cui. 2021. Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce. In SIGMOD. ACM, 2262–2270.
[4]
Pan Zhou, Qian Lin, Dumitrel Loghin, Beng Chin Ooi, Yuncheng Wu, and Hongfang Yu. 2021. Communication-efficient Decentralized Machine Learning over Heterogeneous Networks. In 37th ICDE. IEEE, 384–395.
Index Terms
- Efficient Partial Reduce Across Clouds
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Published In
July 2022
110 pages
ISBN:9781450397483
DOI:10.1145/3542637
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 07 November 2023
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APNet 2022
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