Energy-Efficient GPU Clusters Scheduling for Deep Learning
Peking University
arXiv:2304.06381 [cs.DC], (13 Apr 2023)
@misc{gu2023energyefficient,
title={Energy-Efficient GPU Clusters Scheduling for Deep Learning},
author={Diandian Gu and Xintong Xie and Gang Huang and Xin Jin and Xuanzhe Liu},
year={2023},
eprint={2304.06381},
archivePrefix={arXiv},
primaryClass={cs.DC}
}
Training deep neural networks (DNNs) is a major workload in datacenters today, resulting in a tremendously fast growth of energy consumption. It is important to reduce the energy consumption while completing the DL training jobs early in data centers. In this paper, we propose PowerFlow, a GPU clusters scheduler that reduces the average Job Completion Time (JCT) under an energy budget. We first present performance models for DL training jobs to predict the throughput and energy consumption performance with different configurations. Based on the performance models, PowerFlow dynamically allocates GPUs and adjusts the GPU-level or job-level configurations of DL training jobs. PowerFlow applies network packing and buddy allocation to job placement, thus avoiding extra energy consumed by cluster fragmentations. Evaluation results show that under the same energy consumption, PowerFlow improves the average JCT by 1.57 – 3.39 x at most, compared to competitive baselines.
April 16, 2023 by hgpu
