Optimization of Heterogeneous Systems with AI Planning Heuristics and Machine Learning: A Performance and Energy Aware Approach

TL;DR

This paper introduces a combined AI planning and machine learning approach to efficiently optimize heterogeneous computing systems for performance and energy consumption, significantly reducing evaluation time.

Contribution

It presents a novel method integrating AI heuristics and machine learning to quickly identify near-optimal system configurations for heterogeneous systems.

Findings

Achieves near-optimal configurations by evaluating only about 7% of possibilities.

Machine learning model speeds up performance and energy estimation over 1000 times.

Effective for systems with GPU or Intel Xeon Phi accelerators.

Abstract

Heterogeneous computing systems provide high performance and energy efficiency. However, to optimally utilize such systems, solutions that distribute the work across host CPUs and accelerating devices are needed. In this paper, we present a performance and energy aware approach that combines AI planning heuristics for parameter space exploration with a machine learning model for performance and energy evaluation to determine a near-optimal system configuration. For data-parallel applications our approach determines a near-optimal host-device distribution of work, number of processing units required and the corresponding scheduling strategy. We evaluate our approach for various heterogeneous systems accelerated with GPU or the Intel Xeon Phi. The experimental results demonstrate that our approach finds a near-optimal system configuration by evaluating only about 7% of reasonable configurations. Furthermore, the performance per Joule estimation of system configurations using our machine learning model is more than 1000x faster compared to the system evaluation by program execution.