GPARS: Graph predictive algorithm for efficient resource scheduling in heterogeneous GPU clusters

Efficient resource scheduling in heterogeneous graphics processing unit (GPU) clusters are critical for maximizing system performance and optimizing resource utilization. However, prior research in resource scheduling algorithms typically employed machine learning (ML) algorithms to estimate job dur...

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Veröffentlicht in:	Future generation computer systems Jg. 152; S. 127 - 137
Hauptverfasser:	Wang, Sheng, Chen, Shiping, Shi, Yumei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.03.2024
Schlagworte:	Graph attention networks Heterogeneous GPU clusters Job duration Resource scheduling Resource utilization Waiting time Graph attention networks Resource scheduling Resource utilization Heterogeneous GPU clusters Job duration Waiting time
ISSN:	0167-739X, 1872-7115
Online-Zugang:	Volltext
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