Supercomputing SC22 - I presented the paper that we wrote together with Ben van Werkhoven, Bram Veenboer and Joost Batenburg. The paper presents our latest research on improving the energy efficiency of GPU applications using auto-tuning. In particular, we developed a performance model to steer the auto-tuner to the most interesting regions of the search space.
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.
Citation
Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning
R. Schoonhoven, B. Veenboer, B. van Werkhoven, K. J. Batenburg
International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) at Supercomputing (SC22) 2022
https://doi.org/10.1109/PMBS56514.2022.00010
