Learning based application driven energy aware compilation for GPU

Abstract

The choice of correct compiler optimization is one of the major factors that determines the performance and power consumption of an application when executed on a target hardware. However, making such a choice is challenging since it requires knowledge of target architecture, code features and compiler pass interdependence. Moreover, the onus of choosing these optimization passes lie on the software developers who rely on heuristic solutions which often yield sub-optimal results. In this paper we proposed a machine learning based mechanism of determining the most energy aware compiler setting for a GPU application. Our proposed technique serves a dual purpose of reducing the energy consumption during execution on one hand and relieving the application developers from making the complex choices on the other. Our proposed approach displayed an average improvement of 11.04% and 5.25% improvement in power and energy efficiency respectively with respect to state-of-art techniques.