利用系统级功耗管理探索能效前沿
Exploring the Frontiers of Energy Efficiency using Power Management at System Scale
AI总结：
本文提出遥测数据驱动的节能预测方法，结合GPU基准测试分析应用功耗，并在Frontier超级计算机上验证，揭示资源受限作业最高可节能8.5%且不影响性能。
摘要：
面对百亿亿次HPC系统激增的功耗需求，本工作应对一项关键挑战：理解由软件驱动的功耗管理技术（如动态电压频率调节（DVFS）和功率封顶）的影响。这些技术在过去几十年中得到了积极发展。通过结合来自GPU基准测试的洞见以理解应用功耗画像，我们提出了一种由遥测数据驱动的方法，用于推导节能预测。该方法已被证实地大规模应用于Frontier超级计算机。我们基于三个月遥测数据的发现表明，对于某些资源受限作业，可在不损害性能的情况下实现显著节能（最高达8.5%）。这转化为可观的成本降低，相当于节省1438 MWh能源。本工作的关键贡献在于为这些最佳情形建立上限的方法论及其成功应用。本工作揭示了潜在节能空间，并使HPC专业人员能够在受限功耗预算内优化功耗—性能权衡，不仅服务于百亿亿次时代，也面向其未来。
英文摘要：
In the face of surging power demands for exascale HPC systems, this work tackles the critical challenge of understanding the impact of software-driven power management techniques like Dynamic Voltage and Frequency Scaling (DVFS) and Power Capping. These techniques have been actively developed over the past few decades. By combining insights from GPU benchmarking to understand application power profiles, we present a telemetry data-driven approach for deriving energy savings projections. This approach has been demonstrably applied to the Frontier supercomputer at scale. Our findings based on three months of telemetry data indicate that, for certain resource-constrained jobs, significant energy savings (up to 8.5%) can be achieved without compromising performance. This translates to a substantial cost reduction, equivalent to 1438 MWh of energy saved. The key contribution of this work lies in the methodology for establishing an upper limit for these best-case scenarios and its successful application. This work sheds light on potential energy savings and empowers HPC professionals to optimize the power-performance trade-off within constrained power budgets, not only for the exascale era but also beyond.
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。
