Power-Capping Metric Evaluation for Improving Energy Efficiency in HPC Applications

Conference · Sat Nov 01 04:00:00 EDT 2025

DOI:https://doi.org/10.1007/978-3-032-07612-0_18· OSTI ID:3007918

Patrou, Maria ^[1]; Wang, Thomas ^[2]; Elwasif, Wael ^[1]; Eisenbach, Markus ^[1]; Miller, Ross ^[1]; Hernandez Mendoza, Oscar ^[1]; Godoy, William ^[1]

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1. ORNL
2. Camas High School

With high-performance computing systems now running at exascale, optimizing power-scaling management and resource utilization has become more critical than ever. This paper explores runtime power-capping optimizations that leverage integrated CPU-GPU power management on architectures like the NVIDIA GH200 superchip. We evaluate energy-performance metrics that account for simultaneous CPU and GPU power-capping effects by using two complementary approaches: speedup-energy-delay and a Euclidean distance-based multi-objective optimization method. By targeting a mostly compute-bound exascale science application, the Locally Self-Consistent Multiple Scattering (LSMS), we explore challenging scenarios to identify potential opportunities for energy savings in exascale applications, and we recognize that even modest reductions in energy consumption can have significant overall impacts. Our results highlight how GPU task-specific dynamic power-cap adjustments combined with integrated CPU-GPU power steering can improve the energy utilization of certain GPU tasks, thereby laying the groundwork for future adaptive optimization strategies.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 3007918
• Resource Type: Conference paper/presentation
• Conference Information: ISC High Performance Computing: Workshop on Energy Efficiency with Sustainable Performance: Techniques, Tools, and Best Practices (EESP25) - Hamburg, Germany, Germany - 6/13/2025
• Country of Publication: United States
• Language: English