分布式机器学习网络拓扑生成的统一、灵活框架

Proceedings of the 7th Asia-Pacific Workshop on Networking Pub Date : 2023-06-29 DOI:10.1145/3600061.3603132

Jianhao Liu, Xiaoyan Li, Yanhua Liu, Weibei Fan

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引用次数: 0

摘要

本文采用自顶向下的设计方法和组合设计理论，提出了一种统一的DML服务器中心网络拓扑设计框架。仿真结果表明，该框架能够有效地支持各种DML任务。我们的框架可以生成兼容的拓扑，以满足各种资源约束和不同的DML任务。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Unified, Flexible Framework in Network Topology Generation for Distributed Machine Learning

In this study, we propose a unified framework for designing a class of server-centric network topologies for DML by adopting top-down design method and combinatorial design theory. Simulation results show that this flexible framework is capable of effectively supporting various DML tasks. Our framework can generate compatible topologies that meet various resource constraints and different DML tasks.

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Proceedings of the 7th Asia-Pacific Workshop on Networking

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