Improvement of power-performance efficiency for high-end computing

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. Recent work has shown application characteristics of single-processor, memory-bound non-interactive codes and distributed, interactive Web services can be exploited to conserve power and energy with minimal performance impact. Our novel approach is to exploit parallel performance inefficiencies characteristic of non-interactive, distributed scientific applications, conserving energy using DVS (dynamic voltage scaling) without impacting time-to-solution (ITS) significantly, reducing cost and improving reliability. We present a software framework to analyze and optimize distributed power-performance using DVS implemented on a 16-node Centrino-based cluster. Using various DVS strategies we achieve application-dependent overall system energy savings as large as 25% with as little as 2% performance impact.