Utilizing Hardware Performance Counters to Model and Optimize the Energy and Performance of Large Scale Scientific Applications on Power-Aware Supercomputers

Abstract

Hardware performance counters are used as effective proxies to estimate power consumption and runtime. In this paper we present a performance counter-based power and performance modeling and optimization method, and use the method to model four metrics: runtime, system power, CPU power and memory power. The performance counters that compose the models are used to explore some counter-guided optimizations with two large-scale scientific applications: an earthquake simulation and an aerospace application. We demonstrate the use of the method using two power-aware supercomputers, Mira at Argonne National Laboratory and SystemG at Virginia Tech. The counter-guided optimizations result in a reduction in energy by an average of 18.28% on up to 32,768 cores on Mira and 11.28% on up to 128 cores on SystemG for the aerospace application. For the earthquake simulation, the average energy reductions achieved are 48.65% on up to 4,096 cores on Mira and 30.67% on up to 256 cores on SystemG.