Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem-Reference-Cited by-同舟云学术

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Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem

Published:2024-08-04 Issue: Volume:2 Page:16-37
ISSN:
Container-title:Proceedings of the ACM SIGCOMM 2024 Conference
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Author:

Liu Xuting¹^ORCID,Arzani Behnaz²^ORCID,Kakarla Siva Kesava Reddy²^ORCID,Zhao Liangyu³^ORCID,Liu Vincent¹^ORCID,Castro Miguel⁴^ORCID,Kandula Srikanth⁵^ORCID,Marshall Luke⁶^ORCID

Affiliation:

1. University of Pennsylvania, Philadelphia, United States of America

2. Microsoft Research, Redmond, United States of America

3. University of Washington, Seattle, USA

4. OpenAI, San Francisco, USA

5. Microsoft, Redmond, United States of America

6. Microsoft Research, Redmond, USA

Funder

NSF (National Science Foundation)

Microsoft Research

University of Pennsylvania

University of Washington

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3651890.3672249

Reference36 articles.

1. Firas Abuzaid, Srikanth Kandula, Behnaz Arzani, Ishai Menache, Matei Zaharia, and Peter Bailis. 2021. Contracting wide-area network topologies to solve flow problems quickly. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21). 175--200.

2. AMD 2023. AMD Instinct MI250 GPUs (slide 18). https://hc34.hotchips.org/assets/program/conference/day1/GPU%20HPC/HC2022.AMD.AlanSmith.v14.Final.20220820.pdf

3. Azure NDv2-series 2022. Azure NDv2-series. https://learn.microsoft.com/en-us/azure/virtual-machines/ndv2-series

4. Dimitris Bertsimas and John N Tsitsiklis. 1997. Introduction to linear optimization. Vol. 6. Athena Scientific Belmont, MA.

5. Synthesizing optimal collective algorithms

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