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Online Evolutionary Batch Size Orchestration for Scheduling Deep Learning Workloads in GPU Clusters
Event Type
Paper
Tags
Accelerator-based Architectures
Resource Management and Scheduling
Reproducibility Badges
Registration Categories
TP
TimeThursday, 18 November 20214pm - 4:30pm CST
Location230-231-232
DescriptionEfficient GPU resource scheduling is essential to maximize resource utilization and save training costs for the increasing amount of deep learning workloads in shared GPU clusters. Existing GPU schedulers largely rely on static policies to leverage the performance characteristics of deep learning jobs. They can hardly reach optimal efficiency, however, due to the lack of elasticity. To address the problem, we propose ONES, an ONline Evolutionary Scheduler for elastic batch size orchestration. ONES automatically manages the elasticity of each job based on the training batch size, so as to maximize GPU utilization and improve scheduling efficiency. It determines the batch size for each job through an online evolutionary search that can continuously optimize the scheduling decisions. We evaluate the effectiveness of ONES with 64 GPUs on TACC's Longhorn supercomputers. The results show that ONES can outperform the prior deep learning schedulers with a significantly shorter average job completion time.
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