The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: PMBS22: The 13th International Workshop on Performance Modeling, Benchmarking, and Simulation of High-Performance Computer Systems
Authors: Richard Schoonhoven (Centrum Wiskunde and Informatica (CWI), Netherlands; Leiden Institute of Advanced Computer Science (LIACS)); Bram Veenboer (Netherlands Institute for Radio Astronomy (ASTRON)); Ben van Werkhoven (Netherlands eScience Center; Centrum Wiskunde and Informatica (CWI), Netherlands); and Kees Joost Batenburg (Leiden Institute of Advanced Computer Science (LIACS); Centrum Wiskunde and Informatica (CWI), Netherlands)
Abstract: Graphics Processing Units (GPUs) have revolutionized the computing landscape over the past decade. However, the growing energy demands of data centers 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.