面向AI的可持续超级计算：HPC规模下的GPU功率上限
Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale
AI总结：
本文在研究型超级计算中心分析GPU功率上限的系统级温度、功耗与作业性能影响，指出其可节能延寿，但用户补偿性提交作业可能抵消收益。
摘要：
随着AI研究与部署的增长，支撑并维持其进步所需的计算负担也不可避免地增加。为了训练或微调NLP、计算机视觉等领域的最先进模型，某种形式的AI硬件加速几乎是必需的。近期的大型语言模型需要大量资源来训练和部署，从而造成巨大的能源消耗、潜在的碳排放，以及对GPU和其他硬件加速器的庞大需求。然而，这一激增对HPC/数据中心层面的能源可持续性具有重大影响。本文在一个研究型超级计算中心研究了对GPU施加功率上限对GPU温度和功耗的聚合影响。我们表明，通过适当程度的功率上限，温度和功耗都会显著下降，从而减少电力消耗，并可能在对作业性能影响极小的情况下延长硬件寿命。虽然功率上限按设计会降低功耗，但其对整体能源消耗的全系统聚合影响尚不那么明确；例如，如果用户注意到GPU功率上限导致作业性能下降，他们可能会申请额外的GPU作业来补偿，从而抵消任何能源节约，甚至使能源消耗恶化。据我们所知，我们的工作是首个在超级计算规模上开展并公开GPU功率上限影响详细分析的研究。我们希望本工作能够激励HPC/数据中心进一步探索、评估并交流对AI硬件加速器施加功率上限的影响，以实现更可持续的AI。
英文摘要：
As research and deployment of AI grows, the computational burden to support and sustain its progress inevitably does too. To train or fine-tune state-of-the-art models in NLP, computer vision, etc., some form of AI hardware acceleration is virtually a requirement. Recent large language models require considerable resources to train and deploy, resulting in significant energy usage, potential carbon emissions, and massive demand for GPUs and other hardware accelerators. However, this surge carries large implications for energy sustainability at the HPC/datacenter level. In this paper, we study the aggregate effect of power-capping GPUs on GPU temperature and power draw at a research supercomputing center. With the right amount of power-capping, we show significant decreases in both temperature and power draw, reducing power consumption and potentially improving hardware life-span with minimal impact on job performance. While power-capping reduces power draw by design, the aggregate system-wide effect on overall energy consumption is less clear; for instance, if users notice job performance degradation from GPU power-caps, they may request additional GPU-jobs to compensate, negating any energy savings or even worsening energy consumption. To our knowledge, our work is the first to conduct and make available a detailed analysis of the effects of GPU power-capping at the supercomputing scale. We hope our work will inspire HPCs/datacenters to further explore, evaluate, and communicate the impact of power-capping AI hardware accelerators for more sustainable AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
