Online Evolutionary Batch Size Orchestration for Scheduling Deep Learning Workloads in GPU Clusters

Autor:	Bian, Zhengda, Li, Shenggui, Wang, Wei, You, Yang
Rok vydání:	2021
Předmět:	Computer Science - Distributed Parallel and Cluster Computing Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Efficient 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. However, they can hardly reach optimal efficiency 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. Comment: This paper has been accepted by the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC21), Nov 14-19, 2021, St. Louis, USA
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2108.03645 Zobrazit plný text záznamu View this record from Arxiv