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Graphics Processing Units (GPUs) have revolutionized the computing landscapeover the past decade. However, the growing energy demands of data centres andcomputing facilities equipped with GPUs come with significant capital andenvironmental costs. The energy consumption of GPU applications greatly dependon how well they are optimized. Auto-tuning is an effective and commonlyapplied technique of finding the optimal combination of algorithm, application,and hardware parameters to optimize performance of a GPU application. In thispaper, we introduce new energy monitoring and optimization capabilities inKernel Tuner, a generic auto-tuning tool for GPU applications. Thesecapabilities enable us to investigate the difference between tuning forexecution time and various approaches to improve energy efficiency, andinvestigate the differences in tuning difficulty. Additionally, our model forGPU power consumption greatly reduces the large tuning search space byproviding clock frequencies for which a GPU is likely most energy efficient.

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