RCHC: A Holistic Runtime System for Concurrent Heterogeneous Computing

Abstract

Concurrent heterogeneous computing (CHC) is rapidly emerging as a promising solution for high-performance and energy-efficient computing. The fundamental challenges for efficient CHC are how to partition the workload of the target application across the devices in the underlying CHC system and how to control the operating frequency of each device in order to maximize the overall efficiency. Despite the extensive prior work on the system software techniques for CHC, efficient runtime support for CHC that robustly supports both functional and performance heterogeneity without the need for extensive offline profiling still remains unexplored. To bridge this gap, we propose RCHC, a holistic runtime system for concurrent heterogeneous computing. RCHC dynamically profiles the target application and constructs the performance and power estimation models based on the runtime information. Guided by the estimation models, RCHC explores the system state space, determines the best system state that is expected to maximize the efficiency of the target application, and accordingly executes it. Our experimental results demonstrate that RCHC significantly outperforms the baseline version (e.g., 61.0% higher energy efficiency on average) that employs the GPU and achieves the efficiency comparable with that of the static best version, which requires extensive offline profiling.