Optimal Resource Efficiency with Fairness in Heterogeneous GPU Clusters

Ensuring the highest training throughput to maximize resource efficiency, while maintaining fairness among users, is critical for deep learning (DL) training in heterogeneous GPU clusters. However, current DL schedulers provide only limited fairness properties and suboptimal training throughput, imp...

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Bibliographische Detailangaben

Hauptverfasser:	Mo, Zizhao, Xu, Huanle, Lau, Wing Cheong
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Distributed, Parallel, and Cluster Computing
Online-Zugang:	Volltext bestellen
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