Workshops Archive

Characterizing the Impact of GPU Power Management on an Exascale System

Workshop: PMBS25: The 16th International Workshop on Performance Modeling, Benchmarking, and Simulation of High-Performance Computer Systems

Authors: Mariana Costa (UFRGS); Antigoni Georgiadou and James B. White III (Oak Ridge National Laboratory (ORNL)); Bruno Villasenor Alvarez and Jordà Polo (Advanced Micro Devices, Inc. (AMD)); Woong Shin (Oak Ridge National Laboratory (ORNL)); Philippe O. A. Navaux (UFRGS); Bronson Messer (Oak Ridge National Laboratory (ORNL)); and Arthur Lorenzon (Federal University of Rio Grande do Sul, Brazil)

Abstract: As GPU-accelerated high-performance computing (HPC) systems approach exascale performance, controlling energy consumption without compromising throughput is essential. Architectures such as the AMD MI250X-based Frontier supercomputer provide runtime mechanisms like frequency and power capping, enabling energy tuning without modifying application code. Although both target energy reduction, they operate via distinct hardware control paths and influence workloads differently. We present a comprehensive evaluation of these strategies on a leadership-class system using diverse HPC proxy applications representative of production workloads. Our study analyzes performance–energy trade-offs across multiple capping levels, node counts (1 and 32), and application profiles. Results show that frequency capping generally achieves higher energy efficiency and scalability, with gains of up to 13.2% without performance loss, while power capping is more effective for single-node runs or bursty GPU utilization. We also provide practical guidelines to help system administrators and users balance energy efficiency and performance in large-scale scientific workloads.

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