Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Abstract

Graphics Processing Units (GPUs) have revolutionized the computing landscape over the past decade. However, the growing energy demands of data centres and computing facilities equipped with GPUs come with significant capital and environmental costs. The energy consumption of GPU applications greatly depend on how well they are optimized. Auto-tuning is an effective and commonly applied technique of finding the optimal combination of algorithm, application, and hardware parameters to optimize performance of a GPU application. In this paper, we introduce new energy monitoring and optimization capabilities in Kernel Tuner, a generic auto-tuning tool for GPU applications. These capabilities enable us to investigate the difference between tuning for execution time and various approaches to improve energy efficiency, and investigate the differences in tuning difficulty. Additionally, our model for GPU power consumption greatly reduces the large tuning search space by providing clock frequencies for which a GPU is likely most energy efficient.

Authors 4

1. Richard Schoonhoven Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica

   Affiliation as printed

   Computational Imaging Group,Centrum Wiskunde & Informatica,Amsterdam,Netherlands

   Leiden Institute of Advanced Computer Science, Leiden, Netherlands

2. Bram Veenboer

   Netherlands Institute for Radio Astronomy

   Affiliation as printed

   Netherlands Institute for Radio Astronomy (ASTRON),Dwingelo,Netherlands

   Netherlands Institute for Radio Astronomy (ASTRON), Dwingelo, Netherlands

3. Ben van Werkhoven

   Centrum Wiskunde & Informatica · Netherlands eScience Center

   Affiliation as printed

   Computational Imaging Group,Centrum Wiskunde & Informatica,Amsterdam,Netherlands

   Netherlands eScience Center, Amsterdam, Netherlands

4. Kees Joost Batenburg Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica

   Affiliation as printed

   Computational Imaging Group,Centrum Wiskunde & Informatica,Amsterdam,Netherlands

   Leiden Institute of Advanced Computer Science, Leiden, Netherlands