Optimize resource placement for in-network computing

Chang Liu; Bin Qin; Wenfei Wu; Jiewen Huang; Wei Nie

doi:10.1117/12.2631123

18 March 2022 Optimize resource placement for in-network computing

Chang Liu, Bin Qin, Wenfei Wu, Jiewen Huang, Wei Nie

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 121680R (2022) https://doi.org/10.1117/12.2631123
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

In this paper, a distributed machine learning algorithm is proposed to optimize the placement of in-network computing resources in network computing by redesigning aggregator hash mapping. Based on this placement method, the three models were tested, and the training task throughput increased by 6.81%, 0.39% and 3.13%, respectively, while the task completion time decreased by 6.82%, 0.28% and 3.31%, respectively. At the same time, based on the placement method, the network computing training system can support more flexible network computing resource allocation mode.

Citation Download Citation

Chang Liu, Bin Qin, Wenfei Wu, Jiewen Huang, and Wei Nie "Optimize resource placement for in-network computing", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 121680R (18 March 2022); https://doi.org/10.1117/12.2631123

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