RPPC: a Holistic Runtime System for Maximizing Performance under Power Capping

Abstract

Maximizing performance in power-constrained computing environments is highly important in cloud and datacenter computing. To achieve the best possible performance of parallel applications under power capping, it is crucial to execute them with the optimal concurrency level and cross-component power allocation between CPUs and memory. Despite extensive prior works, it still remains unexplored to investigate the efficient runtime support that maximizes the performance of parallel applications under power capping through the coordinated control of concurrency level and cross-component power allocation. To bridge this gap, this work proposes RPPC, a holistic runtime system for maximizing performance under power capping. In contrast to the state-of-the-art techniques, RPPC robustly controls the two performance-critical knobs (i.e., concurrency level and cross-component power allocation) in a coordinated manner to maximize the performance of parallel applications under power capping. RPPC dynamically identifies the characteristics of the target parallel application and explores the system state space to find an efficient system state. Our experimental results demonstrate that RPPC significantly outperforms the two state-of-the-art power-capping techniques, achieves the performance comparable with the static best version that requires extensive per-application offline profiling, incurs small performance overheads, and provides the re-adaptation mechanism to external events such as total power budget changes.