Topology-aware GPU scheduling for learning workloads in cloud environments

Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud, are enabling deep learning in various domains including health care, autonomous vehicles, and Internet of Things. Multi-GPU systems exhibit complex connectivity among GPUs and between GPUs and CPUs....

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Veröffentlicht in:	International Conference for High Performance Computing, Networking, Storage and Analysis (Online) S. 1 - 12
Hauptverfasser:	Amaral, Marcelo, Polo, Jordà, Carrera, David, Seelam, Seetharami, Steinder, Malgorzata
Format:	Tagungsbericht Verlag
Sprache:	Englisch
Veröffentlicht:	New York, NY, USA ACM 12.11.2017 Association for Computing Machinery (ACM)
Schriftenreihe:	ACM Conferences
Schlagworte:	Algorismes paral·lels Cloud computing Computer systems organization > Architectures > Distributed architectures > Cloud computing Deep Learning GPU Graphics processing units High performance computing Informàtica Interference Machine learning algorithms Multi-GPU Parallel algorithms Performance Analysis Placement Prototypes Resource Contention Scheduling Supercomputadors Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis > Scheduling algorithms Theory of computation > Design and analysis of algorithms > Graph algorithms analysis Theory of computation > Theory and algorithms for application domains > Machine learning theory Workload Interference Workload Interference and Deep Learning Àrees temàtiques de la UPC multi-GPU resource contention scheduling placement GPU performance analysis workload interference and deep learning
ISBN:	9781450351140, 145035114X
ISSN:	2167-4337
Online-Zugang:	Volltext
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