Performance-constrained Distributed DVS Scheduling for Scientific Applications on Power-aware Clusters

  • Authors:

  • Rong Ge;Xizhou Feng;Kirk W. Cameron

  • Affiliations:

  • University of South Carolina, Columbia;University of South Carolina, Columbia;University of South Carolina, Columbia

  • Venue:
  • SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
  • Year:
  • 2005

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Abstract

Left unchecked, the fundamental drive to increase peak performance using tens of thousands of power hungry components will lead to intolerable operating costs and failure rates. High-performance, power-aware distributed computing reduces power and energy consumption of distributed applications and systems without sacrificing performance. Generally, we use DVS (Dynamic Voltage Scaling) technology now available in high-performance microprocessors to reduce power consumption during parallel application runs when peak CPU performance is not necessary due to load imbalance, communication delays, etc. We propose distributed performance-directed DVS scheduling strategies for use in scalable power-aware HPC clusters. By varying scheduling granularity we can obtain significant energy savings without increasing execution time (36% for FT from NAS PB). We created a software framework to implement and evaluate our various techniques and show performance-directed scheduling consistently saves more energy (nearly 25% for several codes) than comparable approaches with less impact on execution time (