Predicting power consumption of GPUs with fuzzy wavelet neural networks

摘要

Prediction and optimization of power consumption have become an essential issue in the field of General-purpose computing on graphic processing units (GPUs) because of the increasing prevalence of GPUs and the constraints of energy consumption. However, previous approaches to build power models need to extract program features related to power consumption from the performance counter or GPU emulators. These approaches are unable to estimate power consumption of applications for software designers owing to the lack of information about detailed GPUs architecture or the supporting of emulators. In this study, we explore a novel method to model GPU power consumption of applications in computing process. By using program slicing, we decompose the source code of applications into slices and extract the power-related static program features. The slicing is used as a basic unit to train a power model based on fuzzy wavelet artificial neural networks. This step allows programmers to investigate the power profile of their applications and identify the code areas with higher energy consumption. To improve prediction accuracy, we further divide the GPUs applications by the branch structure into two categories: sparseness-branch and denseness-branch. The power model is proposed for sparseness-branch programs based on slicing. For denseness-branch programs, probabilistic slicing is utilized to reduce the invalid slices in order to improve accuracy. The models are empirically validated by using typical GPU benchmarks and the results are compared with the measured power. Overall, the average error of our power models is less than 6%. (C) 2015 Elsevier B.V. All rights reserved.