AdapCC: Making Collective Communication in Distributed Machine Learning Adaptive

As deep learning (DL) models continue to grow in size, there is a pressing need for distributed model learning using a large number of devices (e.g., G PU s) and servers. Collective communication among devices/servers (for gradient synchronization, intermediate data exchange, etc.) introduces signif...

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Veröffentlicht in:	Proceedings of the International Conference on Distributed Computing Systems S. 25 - 35
Hauptverfasser:	Zhao, Xiaoyang, Zhang, Zhe, Wu, Chuan
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 23.07.2024
Schlagworte:	collective communication distributed training Graphics processing units Libraries Performance evaluation Pressing Rendering (computer graphics) Throughput Training
ISSN:	2575-8411
Online-Zugang:	Volltext
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