HARS: a Heterogeneity-Aware Runtime System for Self-Adaptive Multithreaded Applications

Abstract

Heterogeneous multi-processing (HMP) is rapidly emerging as a promising solution for high-performance and low-power computing. Despite extensive prior work, system-software support for self-adaptive multithreaded applications has been little explored in the context of HMP. To bridge this gap, we propose HARS, a heterogeneity-aware runtime system for self-adaptive multithreaded applications. HARS continuously monitors the application performance and dynamically adapts the system state to enhance the performance/watt of the target self-adaptive multithreaded applications on HMP systems, while satisfying the user-specified performance goal. We quantify the effectiveness of HARS by demonstrating that HARS achieves significantly higher efficiency than the baseline version with the Linux HMP scheduler and comparable efficiency with that of the static optimal version.