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.

翻译：节能和节能是达到预示系统所需的20兆瓦电力封套的最关键制约因素之一。为此,这一领域的大部分研究侧重于使用用户可控制的硬开关,如每核心动态电压频率缩放和软件控制时钟调控应用级别。在本文件中,我们为“潜望图框架”提出了一个调制插件,将区域一级的精细微微微的自动调与“DVFS”和“非核心频率缩放(UFS)相结合。调试以一个供养向前神经网络为基础,该网络由x86系统支持,并经过标准化基准培训。关于五个标准化混合基准的实验显示,在根据我们的方法调整应用时,能源平均改善16.1%,而静调用率为7.8%。