PS-Hybrid: Hybrid communication framework for large recommendation model training

doi:10.16511/j.cnki.qhdxxb.2021.22.041

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Abstract Most traditional distributed deep learning training systems have been based on parameter servers which have centralized communication architectures that face serious communication bottlenecks due to the large amounts of communications and AllReduce communication frameworks which have decentralized communication architectures that cannot store the entire model due to the large number of parameters. This paper presents PS-Hybrid, a hybrid communication framework, for large deep learning recommendation model training which decouples the communication logic from the embedded parameters and other parameters. Tests show that this prototype system achieves better performance than previous parameter servers for recommendation model training. The system is 48% faster than TensorFlow-PS with 16 computing nodes.

Keywords recommendation model distributed deep learning parameter server AllReduce

Issue Date: 18 August 2022

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	MIAO Xupeng
	ZHANG Minxu
	SHAO Yingxia
	CUI Bin

Cite this article:

MIAO Xupeng,ZHANG Minxu,SHAO Yingxia, et al. PS-Hybrid: Hybrid communication framework for large recommendation model training[J]. Journal of Tsinghua University(Science and Technology), 2022, 62(9): 1417-1425.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2021.22.041 OR http://jst.tsinghuajournals.com/EN/Y2022/V62/I9/1417

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