Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training
Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training
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DOI：
10.48550/arxiv.2208.06102
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Jie You;Jaehoon Chung;Mosharaf Chowdhury
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文献类型：
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作者：
Jie You;Jaehoon Chung;Mosharaf Chowdhury
训练深度神经网络（DNN）每年都变得越来越资源和能源密集。不幸的是，现有的工作主要集中在优化DNN训练，以更快地完成，通常没有考虑对能源效率的影响。在本文中，我们观察到，提高训练性能的常见做法往往会导致能源使用效率低下。更重要的是，我们证明了能源消耗和性能优化之间存在权衡。为此，我们提出了Zeus，一个优化框架，通过自动为重复的DNN训练作业找到最佳的作业和GPU级配置来导航这种权衡。Zeus使用在线探索开发方法与即时能源分析相结合，避免了昂贵的离线测量，同时适应数据随时间的漂移。我们的评估表明，对于不同的工作负载，Zeus可以将DNN训练的能源效率提高15.3%-75.8%。
Training deep neural networks (DNNs) is becoming increasingly more resource- and energy-intensive every year. Unfortunately, existing works primarily focus on optimizing DNN training for faster completion, often without considering the impact on energy efficiency. In this paper, we observe that common practices to improve training performance can often lead to inefficient energy usage. More importantly, we demonstrate that there is a tradeoff between energy consumption and performance optimization. To this end, we propose Zeus, an optimization framework to navigate this tradeoff by automatically finding optimal job- and GPU-level configurations for recurring DNN training jobs. Zeus uses an online exploration-exploitation approach in conjunction with just-in-time energy profiling, averting the need for expensive offline measurements, while adapting to data drifts over time. Our evaluation shows that Zeus can improve the energy efficiency of DNN training by 15.3%-75.8% for diverse workloads.