nvshare: Practical GPU Sharing Without Memory Size Constraints

GPUs are essential for accelerating Machine Learning (ML) work-loads. A common practice is deploying ML jobs as containers managed by an orchestrator such as Kubernetes. Kubernetes schedules GPU workloads by exclusively assigning a device to a single job, which leads to massive GPU underutilization,...

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Veröffentlicht in:	Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) S. 16 - 20
Hauptverfasser:	Alexopoulos, Georgios, Mitropoulos, Dimitris
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 14.04.2024
Schlagworte:	Containers Graphics Processing Unit Graphics processing units Machine learning Memory management Random access memory Reliability Resource Sharing Schedules
ISSN:	2574-1934
Online-Zugang:	Volltext
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