{"title":"分布式机器学习网络拓扑生成的统一、灵活框架","authors":"Jianhao Liu, Xiaoyan Li, Yanhua Liu, Weibei Fan","doi":"10.1145/3600061.3603132","DOIUrl":null,"url":null,"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.","PeriodicalId":228934,"journal":{"name":"Proceedings of the 7th Asia-Pacific Workshop on Networking","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Unified, Flexible Framework in Network Topology Generation for Distributed Machine Learning\",\"authors\":\"Jianhao Liu, Xiaoyan Li, Yanhua Liu, Weibei Fan\",\"doi\":\"10.1145/3600061.3603132\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":228934,\"journal\":{\"name\":\"Proceedings of the 7th Asia-Pacific Workshop on Networking\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 7th Asia-Pacific Workshop on Networking\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3600061.3603132\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 7th Asia-Pacific Workshop on Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3600061.3603132","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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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