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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Vydané v:	Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) s. 16 - 20
Hlavní autori:	Alexopoulos, Georgios, Mitropoulos, Dimitris
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	ACM 14.04.2024
Predmet:	Containers Graphics Processing Unit Graphics processing units Machine learning Memory management Random access memory Reliability Resource Sharing Schedules
ISSN:	2574-1934
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