GreenFlow: A Carbon-Efficient Scheduler for Deep Learning Workloads
Diandian Gu, Yihao Zhao, Peng Sun, Xin Jin, Xuanzhe Liu
Abstract
Deep learning (DL) has become a key component of modern software. Training DL models leads to huge carbon emissions. In data centers, it is important to reduce carbon emissions while completing DL training jobs early. In this article, we propose GreenFlow, a GPU cluster scheduler that reduces the average Job Completion Time (JCT) under a carbon emission budget. We first present performance models for DL training jobs to predict the throughput and energy consumption performance under different configurations. Based on the performance models and the carbon intensity of the grid, GreenFlow dynamically allocates GPUs, and adjusts the GPU-level and job-level configurations of DL training jobs. GreenFlow applies network packing and buddy allocation to job placement, thus avoiding extra carbon incurred by resource fragmentations. Evaluations on a real testbed show that when emitting the same amount of carbon, GreenFlow can improve the average JCT by up to 2.15×, compared to competitive baselines.