信息通信技术与政策

信息通信技术与政策

信息通信技术与政策 ›› 2021, Vol. 47 ›› Issue (3): 83-89.doi: 10.12267/j.issn.2096-5931.2021.03.014

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通信系统优化对分布式机器学习系统性能提升的分析

Analysis of communication system optimizationson performance of distributed machine learning systems

王蕴韬   

  1. 中国信息通信研究院云计算与大数据研究所,北京 100191
  • 出版日期:2021-03-15 发布日期:2021-03-31
  • 作者简介:
    王蕴韬:中国信息通信研究院云计算与大数据研究所副总工程师,国际电信联盟ITU-T Q5/ 16 报告人。主要研究方向为人工智能、区块链等新一代信息通信技术研究、标准化制定及产业发展政策制定等

WANG Yuntao   

  1. Cloud Computing & Big Data Research Institute, China Academy of Information and Communications Technology,Beijing 100191, China
  • Online:2021-03-15 Published:2021-03-31

摘要: 随着人工智能技术的迅猛发展,分布式机器学习系统的应用不断加速,对该系统性能提升的研究愈发紧迫。聚焦用于分布式机器学习的通信系统对整体系统性能提升的重大影响,从机器学习计算的独特性及分布式系统性能现有分析理论的局限性入手,对理论和工程实现两个维度深度分析了通信系统优化对于分布式机器学习系统实现线性乃至超线性加速的可行性,提出了影响分布式机器学习系统性能提升最为关键的三个通信系统优化核心要素,并对机器学习分布式系统中的通信优化理论及未来实践方向作出了展望。

关键词: 人工智能, 通信系统优化, 分布式系统

Abstract: As Artificial Intelligence develops rapidly, utilization of distributed machine learning systems continues to accelerate, and the research on this area is urgent. This paper focuses on the analysis of key factors that communication systems impact the performance of distributed machine learning systems, starting from the analysis of unique features of machine learning computation and limitations of existing theories. Then, it focuses on the feasibility study of linear and super-linear acceleration of distributed machine learning systems, proposes three key factors consisting numbers of communication system optimization technics, and puts forward future prospects of communication optimization theories as well as engineering technics.

Key words: Artificial Intelligence, communication system optimization, distributed system