Modelling DVFS and UFS for Region-Based Energy Aware Tuning of HPC Applications

Mohak Chadha, M. Gerndt
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引用次数: 6

Abstract

Energy efficiency and energy conservation are one of the most crucial constraints for meeting the 20MW power envelope desired for exascale systems. Towards this, most of the research in this area has been focused on the utilization of user-controllable hardware switches such as per-core dynamic voltage frequency scaling (DVFS) and software controlled clock modulation at the application level. In this paper, we present a tuning plugin for the Periscope Tuning Framework which integrates fine-grained autotuning at the region level with DVFS and uncore frequency scaling (UFS). The tuning is based on a feed-forward neural network which is formulated using Performance Monitoring Counters (PMC) supported by x86 systems and trained using standardized benchmarks. Experiments on five standardized hybrid benchmarks show an energy improvement of 16.1% on average when the applications are tuned according to our methodology as compared to 7.8% for static tuning.
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基于区域的高性能计算应用能量感知调谐的DVFS和UFS建模
能源效率和节能是满足百亿亿级系统所需的20MW功率信封的最关键限制之一。为此,该领域的大部分研究都集中在用户可控硬件开关的利用上,如在应用层面上的每核动态电压频率缩放(DVFS)和软件控制时钟调制。在本文中,我们提出了一个用于Periscope调优框架的调优插件,它将区域级的细粒度自动调优与DVFS和非核心频率缩放(UFS)相结合。调优基于前馈神经网络,该网络使用x86系统支持的性能监控计数器(PMC)制定,并使用标准化基准进行训练。在五个标准化混合基准测试上进行的实验表明,根据我们的方法对应用程序进行调优时,能耗平均提高16.1%,而静态调优则为7.8%。
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