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.

翻译：训练深度神经网络（DNNs）是当前数据中心的主要工作负载，导致能耗急剧增长。在数据中心中，如何在尽早完成深度学习训练任务的同时降低能耗至关重要。本文提出PowerFlow——一种在能量预算约束下降低平均作业完成时间（JCT）的GPU集群调度器。我们首先构建深度学习训练任务的性能模型，以预测不同配置下的吞吐量和能耗表现。基于该性能模型，PowerFlow动态分配GPU并调整深度学习训练任务的GPU级或作业级配置。PowerFlow在作业部署中应用网络打包与搭档分配策略，从而避免因集群碎片化产生的额外能耗。评估结果表明，在相同能耗条件下，与竞争基线相比，PowerFlow将平均JCT最多提升1.57至3.39倍。