Accelerating Massively Distributed Deep Learning Through Efficient Pseudo-Synchronous Update Method

IF 0.9 4区计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS

International Journal of Parallel Programming Pub Date : 2023-11-13 DOI:10.1007/s10766-023-00759-4

Yingpeng Wen, Zhilin Qiu, Dongyu Zhang, Dan Huang, Nong Xiao, Liang Lin

引用次数: 0

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利用高效的伪同步更新方法加速大规模分布式深度学习

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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来源期刊

International Journal of Parallel Programming 工程技术-计算机：理论方法

CiteScore

4.40

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： International Journal of Parallel Programming is a forum for the publication of peer-reviewed, high-quality original papers in the computer and information sciences, focusing specifically on programming aspects of parallel computing systems. Such systems are characterized by the coexistence over time of multiple coordinated activities. The journal publishes both original research and survey papers. Fields of interest include: linguistic foundations, conceptual frameworks, high-level languages, evaluation methods, implementation techniques, programming support systems, pragmatic considerations, architectural characteristics, software engineering aspects, advances in parallel algorithms, performance studies, and application studies.