Adagio 论文

摘要

Power and energy are first-order design constraints in high performance computing. Current research using dynamic voltage scaling (DVS) relies on trading increased execution time for energy savings, which is unacceptable for most high performance computing applications. We present Adagio, a novel runtime system that makes DVS practical for complex, real-world scientific applications by incurring only negligible delay while achieving significant energy savings. Adagio improves and extends previous state-of-the-art algorithms by combining the lessons learned from static energy-reducing CPU scheduling with a novel runtime mechanism for slack prediction. We present results using Adagio for two real-world programs, UMT2K and ParaDiS, along with the NAS Parallel Benchmark suite. While requiring no modification to the application source code, Adagio provides total system energy savings of 8% and 20% for UMT2K and ParaDiS, respectively, with less than 1% increase in execution time.