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

Jianhao Liu, Xiaoyan Li, Yanhua Liu, Weibei Fan

Abstract: 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.