CHRT: a Criticality- and Heterogeneity-Aware Runtime System for Task-Parallel Applications

Abstract

Heterogeneous multiprocessing (HMP) is an emerging technology for high-performance and energy-efficient computing. While task parallelism is widely used in various computing domains from the embedded to machine-learning computing domains, relatively little work has been done to investigate the efficient runtime support that effectively utilizes the criticality of the tasks of the target application and the heterogeneity of the underlying HMP system with full resource management. To bridge this gap, we propose a criticality- and heterogeneity-aware runtime system for task-parallel applications (CHRT). CHRT dynamically estimates the performance and power consumption of the target task-parallel application and robustly manages the full HMP system resources (i.e., core types, counts, and voltage/frequency levels) to maximize the overall efficiency. Our experimental results show that CHRT achieves significantly higher energy efficiency than the baseline runtime system that employs the breadth-first scheduler and the state-of-the-art criticality-aware runtime system.