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- W4313479568 abstract "Abstract Large-scale distributed training mainly consists of sub-model parallel training and parameter synchronization. With the expansion of training workers, the efficiency of parameter synchronization will be affected. To tackle this problem, we first propose 2D-TGA, a g rouping A llReduce method based on the two-dimensional t orus topology. This method synchronizes the model parameters by grouping and makes full use of bandwidth. Secondly, we propose a distributed algorithm, 2D-TGA-ADMM, which combines the 2D-TGA with the alternating direction method of multipliers (ADMM). It focuses on sub-model training and reduces the wait time among workers in the synchronization process. Finally, experimental results on the Tianhe-2 supercomputing platform show that compared with the $${mathtt {MPI_Allreduce}}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mi>MPI</mml:mi> <mml:mi>_</mml:mi> <mml:mi>Allreduce</mml:mi> </mml:mrow> </mml:math> , the 2D-TGA could shorten the synchronization wait time by $$33%$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mn>33</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ." @default.
- W4313479568 created "2023-01-06" @default.
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- W4313479568 date "2023-01-02" @default.
- W4313479568 modified "2023-10-14" @default.
- W4313479568 title "A Communication Efficient ADMM-based Distributed Algorithm Using Two-Dimensional Torus Grouping AllReduce" @default.
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- W4313479568 doi "https://doi.org/10.1007/s41019-022-00202-7" @default.
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