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MINA: Auto-scale In-network Aggregation for Machine Learning Service

Published: 05 September 2023 Publication History
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References

[1]
Richard L. Graham, Devendar Bureddy, 2016. Scalable Hierarchical Aggregation Protocol (SHArP): A Hardware Architecture for Efficient Data Reduction. In 2016 COMHPC.
[2]
Ronny Krashinsky, Olivier Giroux, 2020. NVIDIA ampere architecture in-depth. NVIDIA blog: https://devblogs. nvidia. com/nvidia-ampere-architecture-in-depth (2020).
[3]
ChonLam Lao, Yanfang Le, 2021. ATP: In-network Aggregation for Multi-tenant Learning. In 18th USENIX NSDI.
[4]
Mauro Ribeiro, Katarina Grolinger, and Miriam AM Capretz. 2015. Mlaas: Machine learning as a service. In IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE.
[5]
Amedeo Sapio, Marco Canini, 2021. Scaling distributed machine learning with in-network aggregation. In 18th USENIX NSDI.

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APNet '23: Proceedings of the 7th Asia-Pacific Workshop on Networking
June 2023
229 pages
ISBN:9798400707827
DOI:10.1145/3600061
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2023

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APNET 2023
APNET 2023: 7th Asia-Pacific Workshop on Networking
June 29 - 30, 2023
Hong Kong, China

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Overall Acceptance Rate 50 of 118 submissions, 42%

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