Going green: Optimizing GPUs for energy efficiency through model-steered auto-tuning
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.
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|13th IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, PMBS 2022|
Schoonhoven, R.A, Veenboer, A.J.P, van Werkhoven, B.J.C, & Batenburg, K.J. (2023). Going green: Optimizing GPUs for energy efficiency through model-steered auto-tuning. In Proceedings of PMBS 2022: Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 48–59). doi:10.1109/PMBS56514.2022.00010