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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2300684


Automatic parallelism strategy generation with minimal memory redundancy


Author(s):  Yanqi SHI, Peng LIANG, Hao ZHENG, Linbo QIAO, Dongsheng LI

Affiliation(s):  National University of Defense Technology, Changsha 410000, China

Corresponding email(s):   yqshi@nudt.edu.cn, peng_leung@nudt.edu.cn, zhengh@nudt.edu.cn, linboqiao@nudt.edu.cn, lds1201@163.com

Key Words:  Deep learning, Automatic parallelism, Minimal memory redundancy


Yanqi SHI, Peng LIANG, Hao ZHENG, Linbo QIAO, Dongsheng LI. Automatic parallelism strategy generation with minimal memory redundancy[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Yanqi SHI, Peng LIANG, Hao ZHENG, Linbo QIAO, Dongsheng LI",
journal="Frontiers of Information Technology & Electronic Engineering",
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number="-1",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300684"
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%T Automatic parallelism strategy generation with minimal memory redundancy
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%A Peng LIANG
%A Hao ZHENG
%A Linbo QIAO
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%J Journal of Zhejiang University SCIENCE C
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DOI - 10.1631/FITEE.2300684


Abstract: 
Large-scale deep learning (DL) models are trained distributedly due to memory and computing resource limitations. Few existing strategy generation approaches take optimal memory minimization as the objective. To fill this gap, we propose a novel algorithm that generates optimal parallelism strategies with the constraint of minimal memory redundancy. We propose a novel Redundant Memory Cost Model (RMCM) to calculate the memory overhead of each operator in a given parallel strategy. To generate the optimal parallelism strategy, we formulate the parallelism strategy searching problem into an integer linear programming problem and use an efficient solver to find minimal-memory intra-operator parallelism strategies. Furthermore, the proposed algorithm has been extended and implemented in a multi-dimensional parallel training framework and is characterized by the ability of high throughput and minimal memory redundancy. Experimental results demonstrate that our approach achieves significant memory savings of up to 67% compared to the latest Megatron-LM strategies, and has a similar throughput. The principal contribution of the present research lies in its provision of a novel algorithm that optimizes parallelism strategies, reducing memory redundancy in large-scale DL models. In conclusion, our paper introduces a memory-efficient algorithm for generating parallelism strategies, surpassing existing strategies in reducing memory requirements.

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