Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters

• URL: http://arxiv.org/abs/2109.01313v2
• Date: Mon, 6 Sep 2021 01:26:38 GMT
• Title: Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters
• Authors: Qinghao Hu, Peng Sun, Shengen Yan, Yonggang Wen, Tianwei Zhang
• Abstract summary: We present a comprehensive study about the characteristics of Deep Learning jobs and resource management. We introduce a general-purpose framework, which manages resources based on historical data. As case studies, we design: a Quasi-Shortest-Service-First scheduling service, which can minimize the cluster-wide average job completion time by up to 6.5x; and a Cluster Energy Saving service, which improves overall cluster utilization by up to 13%.
• Score: 30.952491139350908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services in both the research community and industry. When operating a datacenter, optimization of resource scheduling and management can bring significant financial benefits. Achieving this goal requires a deep understanding of the job features and user behaviors. We present a comprehensive study about the characteristics of DL jobs and resource management. First, we perform a large-scale analysis of real-world job traces from SenseTime. We uncover some interesting conclusions from the perspectives of clusters, jobs and users, which can facilitate the cluster system designs. Second, we introduce a general-purpose framework, which manages resources based on historical data. As case studies, we design: a Quasi-Shortest-Service-First scheduling service, which can minimize the cluster-wide average job completion time by up to 6.5x; and a Cluster Energy Saving service, which improves overall cluster utilization by up to 13%.

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