Energy Profiling and Analysis of the HPC Challenge Benchmarks

  • Authors:

  • Shuaiwen Song; Rong Ge; Xizhou Feng;Kirk W. Cameron

  • Affiliations:

  • SCAPE LABORATORY, VIRGINIA POLYTECHNIC INSTITUTE ANDSTATE UNIVERSITY, BLACKSBURG, VA 24060, USA;MARQUETTE UNIVERSITY, MILWAULKEE, WI 53233, USA;VIRGINIA BIOINFORMATICS INSTITUTE, VIRGINIA POLYTECHNICINSTITUTE AND STATE UNIVERSITY, BLACKSBURG, VA 24060, USA;SCAPE LABORATORY, VIRGINIA POLYTECHNIC INSTITUTE ANDSTATE UNIVERSITY, BLACKSBURG, VA 24060, USA

  • Venue:
  • International Journal of High Performance Computing Applications
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Future high performance systems must use energy efficiently to achieve petaFLOPS computational speeds and beyond. To address this challenge, we must first understand the power and energy characteristics of high performance computing applications. In this paper, we use a power-performance profiling framework called Power-Pack to study the power and energy profiles of the HPC Challenge benchmarks. We present detailed experimental results along with in-depth analysis of how each benchmark's workload characteristics affect power consumption and energy efficiency. This paper summarizes various findings using the HPC Challenge benchmarks, including but not limited to: 1) identifying application power profiles by function and component in a high performance cluster; 2) correlating applications' memory access patterns to power consumption for these benchmarks; and 3) exploring how energy consumption scales with system size and workload.