Energy-Efficient Computing for GPU Applications

Energy efficiency has become a critical concern in High-Performance Computing (HPC) and Supercomputing, especially with the rise of exascale systems. The increasing demand for computational power and the associated energy consumption have led to a growing need for optimization techniques to reduce power consumption. Graphics Processing Units (GPUs), now the primary source of compute power in exascale supercomputers, contribute significantly to the overall energy expenditure of these systems. Consequently, the development and implementation of energy-efficient strategies for GPU applications are essential to reduce the environmental impact and operational costs of HPC facilities.

This tutorial offers a comprehensive introduction to energy-efficient computing in the context of HPC, focusing on GPU applications. As a participant, you will gain insight into code optimization techniques that improve energy efficiency, automatically explore performance-energy trade-offs using auto-tuning, and learn how to write clean code for reduced-precision arithmetic on GPUs.

Lastly, the tutorial addresses GPU clock frequency optimization to improve energy efficiency, including how to find the optimal core clock frequency range. The hands-on approach of this tutorial enables participants to acquire valuable knowledge and practical experience in energy-efficient computing, essential for advancing environmentally sustainable and cost-effective HPC and Supercomputing solutions.