DVFS-aware application classification to improve GPGPUs energy efficiency

摘要

The increasing importance of GPUs as high-performance accelerators and the power and energy constraints of computing systems, make it fundamental to develop techniques for energy efficiency maximization of GPGPU applications. Among several potential techniques, dynamic voltage and frequency scaling (DVFS) stands out as one of the most promising approaches. Hence, novel DVFS-aware performance and power classification models are herein proposed that correlate application characteristics and GPU architecture features. In particular, by analysing the utilization of graphics and memory components at a single voltage and frequency levels, the proposed classification methodologies are able to predict the impact of DVFS on GPGPU applications execution time and power and energy consumption. The accuracy of the proposed approach is validated on two modern NVIDIA GPUs from the Maxwell and Pascal generations, by relying on 35 benchmarks from the Rodinia, Polybench, Parboil, SHOC and CUDA SDK suites. Experimental results show that the proposed approach can typically predict the optimal operating frequencies of graphics and memory subsystems, attaining up to 36% energy savings (average of 16%), which correspond to an average deviation of 0.74% regarding the optimal case. Moreover, when considering a maximum performance penalty of 10%, up to 26% energy savings are still attained. (C) 2018 Elsevier B.V. All rights reserved.