Energy-efficient Application Resource Scheduling using Machine Learning Classifiers

Resource scheduling in high performance computing (HPC) usually aims to minimize application runtime rather than optimize for energy efficiency. Most existing research on reducing power and energy consumption imposes the constraint that little or no performance loss is allowed, which improves but still does not maximize energy efficiency. By optimizing for energy efficiency instead of application turnaround time, we can reduce the cost of running scientific applications. We propose using machine learning classification, driven by low-level hardware performance counters, to predict the most energy-efficient resource settings to use during application runtime, which unlike static resource scheduling dynamically adapts to changing application behavior. We evaluate our approach on a large shared-memory system using four complex bioinformatic HPC applications, decreasing energy consumption over the naive race scheduler by 20% on average, and by as much as 38%. An average increase in runtime of 31% is dominated by a 39% reduction in power consumption, from which we extrapolate the potential for a 24% increase in throughput for future over-provisioned, power-constrained clusters. This work demonstrates that low-overhead classification is suitable for dynamically optimizing energy efficiency during application runtime.