Modeling and Chasing the Energy-Efficiency Sweet Spots in Modern GPUs

Afzal A, Manfred Li M, Panzlaff M (2026)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2026

Conference Proceedings Title: Lecture Notes in Computer Science

Event location: Poznań, Poland PL

DOI: 10.48550/arXiv.2607.00819

Abstract

Energy consumption is a key limitation in high-performance computing on heterogeneous CPU-GPU systems. This work studies how hardware configuration affects energy-to-solution under realistic workloads. We study energy efficiency regimes using molecular dynamics benchmarks (GROMACS and AMBER) and a stress-test benchmark (FIRESTARTER) on systems with A40, A100, H100, and H200 GPUs and Intel Ice Lake CPU, varying frequency scaling and power cap. We show that energy-to-solution exhibits workload- and architecture-dependent transitions between efficient and inefficient regimes, driven by nonlinear GPU power-frequency scaling. We introduce an interpretable analytical model that decomposes GPU power into linear and nonlinear components, identifying a workload- and architecture-dependent transition frequency beyond which efficiency degrades. The model fits empirical data with low error and highlights the role of baseline power, nonlinear power behavior, and transition frequency as the dominant parameters governing energy efficiency. Power capping is generally less effective for efficiency tuning than frequency reduction, especially for workloads that operate far from thermal design power. Overall, energy-efficient HPC execution is a configuration-dependent problem with identifiable regime shifts, and we provide model-driven guidance for selecting operating points.

Authors with CRIS profile

Ayesha Afzal Erlangen National High Performance Computing Center (NHR@FAU) Michael Panzlaff Erlangen National High Performance Computing Center (NHR@FAU)

How to cite

APA:

Afzal, A., Manfred Li, M., & Panzlaff, M. (2026). Modeling and Chasing the Energy-Efficiency Sweet Spots in Modern GPUs. In Wyrzykowski, R., Deelman, E. (Eds.), Lecture Notes in Computer Science. Poznań, Poland, PL.

MLA:

Afzal, Ayesha, Markus Manfred Li, and Michael Panzlaff. "Modeling and Chasing the Energy-Efficiency Sweet Spots in Modern GPUs." Proceedings of the 16th International Conference on Parallel Processing and Applied Mathematics, PPAM 2026, Poznań, Poland Ed. Wyrzykowski, R., Deelman, E., 2026.

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