2D-THA-ADMM: communication efficient distributed ADMM algorithm framework based on two-dimensional torus hierarchical AllReduce
Model synchronization refers to the communication process involved in large-scale distributed machine learning tasks. As the cluster scales up, the synchronization of model parameters becomes a challenging task that has to be coordinated among thousands of workers. Firstly, this study proposes a h i...
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| Veröffentlicht in: | International journal of machine learning and cybernetics Jg. 15; H. 2; S. 207 - 226 |
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| Sprache: | Englisch |
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01.02.2024
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| ISSN: | 1868-8071, 1868-808X |
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| Abstract | Model synchronization refers to the communication process involved in large-scale distributed machine learning tasks. As the cluster scales up, the synchronization of model parameters becomes a challenging task that has to be coordinated among thousands of workers. Firstly, this study proposes a
h
ierarchical
A
llReduce algorithm structured on a
two
-
d
imensional
t
orus (2D-THA), which utilizes a hierarchical structure to synchronize model parameters and maximize bandwidth utilization. Secondly, this study introduces a distributed consensus algorithm called 2D-THA-ADMM, which combines the 2D-THA synchronization algorithm with the alternating direction method of multipliers (ADMM). Thirdly, we evaluate the model parameter synchronization performance of 2D-THA and the scalability of 2D-THA-ADMM on the Tianhe-2 supercomputing platform using real public datasets. Our experiments demonstrate that 2D-THA significantly reduces synchronization time by
63.447
%
compared to MPI_Allreduce. Furthermore, the proposed 2D-THA-ADMM algorithm exhibits excellent scalability, with a training speed increase of over 3
×
compared to the state-of-the-art methods, while maintaining high accuracy and computational efficiency. |
|---|---|
| AbstractList | Model synchronization refers to the communication process involved in large-scale distributed machine learning tasks. As the cluster scales up, the synchronization of model parameters becomes a challenging task that has to be coordinated among thousands of workers. Firstly, this study proposes a
h
ierarchical
A
llReduce algorithm structured on a
two
-
d
imensional
t
orus (2D-THA), which utilizes a hierarchical structure to synchronize model parameters and maximize bandwidth utilization. Secondly, this study introduces a distributed consensus algorithm called 2D-THA-ADMM, which combines the 2D-THA synchronization algorithm with the alternating direction method of multipliers (ADMM). Thirdly, we evaluate the model parameter synchronization performance of 2D-THA and the scalability of 2D-THA-ADMM on the Tianhe-2 supercomputing platform using real public datasets. Our experiments demonstrate that 2D-THA significantly reduces synchronization time by
63.447
%
compared to MPI_Allreduce. Furthermore, the proposed 2D-THA-ADMM algorithm exhibits excellent scalability, with a training speed increase of over 3
×
compared to the state-of-the-art methods, while maintaining high accuracy and computational efficiency. |
| Author | Lei, Yongmei Peng, Cunlu Zhang, Zeyu Wang, Guozheng |
| Author_xml | – sequence: 1 givenname: Guozheng orcidid: 0000-0002-5260-3458 surname: Wang fullname: Wang, Guozheng organization: School of Computer Engineering and Science, Shanghai University – sequence: 2 givenname: Yongmei surname: Lei fullname: Lei, Yongmei email: Lei@shu.edu.cn organization: School of Computer Engineering and Science, Shanghai University – sequence: 3 givenname: Zeyu surname: Zhang fullname: Zhang, Zeyu organization: School of Computer Engineering and Science, Shanghai University – sequence: 4 givenname: Cunlu surname: Peng fullname: Peng, Cunlu organization: School of Computer Engineering and Science, Shanghai University |
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| Cites_doi | 10.1109/MNET.011.2000530 10.1016/j.jpdc.2008.09.002 10.1109/TNNLS.2021.3051638 10.1016/j.asoc.2022.109051 10.1007/s11227-020-03590-7 10.1109/JPROC.2020.3022687 10.1109/TNET.2021.3117042 10.1177/1094342005051521 10.1016/j.jpdc.2021.05.012 10.1109/TCOMM.2020.3026398 10.1007/s00607-021-00968-0 10.1109/TIFS.2021.3113768 10.1142/S0129626407002880 10.1109/CAHPC.2018.8645857 10.1109/ICASSP.2014.6854796 10.1145/329366.301116 10.1109/TNNLS.2022.3192346 10.1007/978-3-030-30709-7_27 10.1109/ISCA52012.2021.00023 10.1145/1273496.1273567 10.1145/2640087.2644155 10.1145/3448016.3450571 10.1109/CLUSTR.2004.1392611 10.1145/3126908.3126954 10.1109/DAC18072.2020.9218538 10.1109/TPWRS.2022.3162329 10.1109/TPAMI.2023.3243080 10.1007/978-3-642-03770-2_41 |
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| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Hierarchical AllReduce Two-dimensional torus Synchronization algorithm ADMM |
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| Title | 2D-THA-ADMM: communication efficient distributed ADMM algorithm framework based on two-dimensional torus hierarchical AllReduce |
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