GPU power prediction via ensemble machine learning for DVFS space exploration

Abstract

A software-based approach to achieve high performance within a power budget often involves dynamic voltage and frequency scaling (DVFS). Thus, accurately predicting the power consumption of an application at different DVFS levels (or more generally, different processor configurations) is paramount for the energy-efficient functioning of a high-performance computing (HPC) system. The increasing prevalence of graphics processing units (GPUs) in HPC systems presents new challenges in power management, and machine learning presents an unique way to improve the software-based power management of these systems. As such, we explore the problem of GPU power prediction at different DVFS states via machine learning. Specifically, we propose a new ensemble technique that incorporates three machine-learning techniques --- sequential minimal optimization regression, simple linear regression, and decision tree --- to reduce the mean absolute error (MAE) to 3.5%.