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EP4DDL: addressing straggler problem in heterogeneous distributed deep learning

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Abstract

Driven by big data, neural networks evolve more complex and the computing capacity of a single machine is often difficult to meet the demand. Distributed deep learning technology has shown great performance superiority for handling this problem. However, a serious issue in this field is the existence of stragglers, which significantly restricts the performance of the whole system. It is an enormous challenge to fully exploit the computing capacity of the system based on parameter server architecture, especially in a heterogeneous environment. Motivated by this, we designed a method named EP4DDL to minimize the impact of the straggler problem by load balance technique. In a statistical view, the approach introduces a novel metric named performance variance to give a comprehensive inspection of stragglers and employs flexible parallelism techniques for each node. We verify the algorithm on standard benchmarks and demonstrate that it can reduce training time to 57.46%, 24.8%, and 11.5%, respectively, without accuracy loss compared with the FlexRR, Con-SGD, and Falcon.

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Acknowledgements

We would like to thank the anonymous reviewers, whose insightful comments greatly improved the quality of this paper. The work described in this paper was supported in part by the Key Basic Research Program of the China Basic Strengthening Program (2019-JCJQ-ZD-041) and the National Key Research and Development Program of China (2016YFB0200902).

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Author notes

  1. Jingbo Li, Jia Wei and Zheng Wei have contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an Jiaotong University, West Xianning Road, Xi’an, 710049, Shaanxi, China

    Zeyu Ji, Xingjun Zhang, Jingbo Li, Jia Wei & Zheng Wei

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  1. Zeyu Ji
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  2. Xingjun Zhang
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  3. Jingbo Li
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  4. Jia Wei
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Correspondence to Xingjun Zhang.

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Ji, Z., Zhang, X., Li, J. et al. EP4DDL: addressing straggler problem in heterogeneous distributed deep learning. J Supercomput 78, 15663–15680 (2022). https://doi.org/10.1007/s11227-022-04466-8

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