CEML: a Coordinated Runtime System for Efficient Machine Learning on Heterogeneous Computing Systems

Abstract

Heterogeneous computing is rapidly emerging as a promising solution for efficient machine learning. Despite the extensive prior works, system software support for efficient machine learning still remains unexplored in the context of heterogeneous computing. To bridge this gap, we propose CEML, a coordinated runtime system for efficient machine learning on heterogeneous computing systems. CEML dynamically analyzes the performance and power characteristics of the target machine-learning application and robustly adapts the system state to enhance its efficiency on heterogeneous computing systems. Our quantitative evaluation demonstrates that CEML significantly improves the efficiency of machine-learning applications on a full heterogeneous computing system.