Characterizing GPU Energy Usage in Exascale-Ready Portable Science Applications
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arXiv
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| Format: | Preprint |
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2025
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| author | Godoy, William F. Hernandez, Oscar Kent, Paul R. C. Patrou, Maria Asifuzzaman, Kazi Miniskar, Narasinga Rao Valero-Lara, Pedro Vetter, Jeffrey S. Sinclair, Matthew D. Lowe-Power, Jason Bruce, Bobby R. |
| author_facet | Godoy, William F. Hernandez, Oscar Kent, Paul R. C. Patrou, Maria Asifuzzaman, Kazi Miniskar, Narasinga Rao Valero-Lara, Pedro Vetter, Jeffrey S. Sinclair, Matthew D. Lowe-Power, Jason Bruce, Bobby R. |
| contents | We characterize the GPU energy usage of two widely adopted exascale-ready applications representing two classes of particle and mesh solvers: (i) QMCPACK, a quantum Monte Carlo package, and (ii) AMReXCastro, an adaptive mesh astrophysical code. We analyze power, temperature, utilization, and energy traces from double-/single (mixed)-precision benchmarks on NVIDIA's A100 and H100 and AMD's MI250X GPUs using queries in NVML and rocm_smi_lib, respectively. We explore application-specific metrics to provide insights on energy vs. performance trade-offs. Our results suggest that mixed-precision energy savings range between 6-25% on QMCPACK and 45% on AMReX-Castro. Also, we found gaps in the AMD tooling used on Frontier GPUs that need to be understood, while query resolutions on NVML have little variability between 1 ms-1 s. Overall, application level knowledge is crucial to define energy-cost/science-benefit opportunities for the codesign of future supercomputer architectures in the post-Moore era. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05623 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Characterizing GPU Energy Usage in Exascale-Ready Portable Science Applications Godoy, William F. Hernandez, Oscar Kent, Paul R. C. Patrou, Maria Asifuzzaman, Kazi Miniskar, Narasinga Rao Valero-Lara, Pedro Vetter, Jeffrey S. Sinclair, Matthew D. Lowe-Power, Jason Bruce, Bobby R. Performance Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing We characterize the GPU energy usage of two widely adopted exascale-ready applications representing two classes of particle and mesh solvers: (i) QMCPACK, a quantum Monte Carlo package, and (ii) AMReXCastro, an adaptive mesh astrophysical code. We analyze power, temperature, utilization, and energy traces from double-/single (mixed)-precision benchmarks on NVIDIA's A100 and H100 and AMD's MI250X GPUs using queries in NVML and rocm_smi_lib, respectively. We explore application-specific metrics to provide insights on energy vs. performance trade-offs. Our results suggest that mixed-precision energy savings range between 6-25% on QMCPACK and 45% on AMReX-Castro. Also, we found gaps in the AMD tooling used on Frontier GPUs that need to be understood, while query resolutions on NVML have little variability between 1 ms-1 s. Overall, application level knowledge is crucial to define energy-cost/science-benefit opportunities for the codesign of future supercomputer architectures in the post-Moore era. |
| title | Characterizing GPU Energy Usage in Exascale-Ready Portable Science Applications |
| topic | Performance Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.05623 |