GPU Acceleration of Monte Carlo Tallies on Unstructured Meshes in OpenMC with PUMI-Tally
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908339912310784 |
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| author | Hasan, Fuad Smith, Cameron W. Shephard, Mark S. Churchill, R. Michael Wilkie, George J. Romano, Paul K. Shriwise, Patrick C. Merson, Jacob S. |
| author_facet | Hasan, Fuad Smith, Cameron W. Shephard, Mark S. Churchill, R. Michael Wilkie, George J. Romano, Paul K. Shriwise, Patrick C. Merson, Jacob S. |
| contents | Unstructured mesh tallies are a bottleneck in Monte Carlo neutral particle transport simulations of fusion reactors. This paper introduces the PUMI-Tally library that takes advantage of mesh adjacency information to accelerate these tallies on CPUs and GPUs. For a fixed source simulation using track-length tallies, we achieved a speed-up of 19.7X on an NVIDIA A100, and 9.2X using OpenMP on 128 threads of two AMD EPYC 7763 CPUs on NERSC Perlmutter. On the Empire AI alpha system, we achieved a speed-up of 20X using an NVIDIA H100 and 96 threads of an Intel Xenon 8568Y+. Our method showed better scaling with number of particles and number of elements. Additionally, we observed a 199X reduction in the number of allocations during initialization and the first three iterations, with a similar overall memory consumption. And, our hybrid CPU/GPU method demonstrated a 6.69X improvement in the energy consumption over the current approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19048 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU Acceleration of Monte Carlo Tallies on Unstructured Meshes in OpenMC with PUMI-Tally Hasan, Fuad Smith, Cameron W. Shephard, Mark S. Churchill, R. Michael Wilkie, George J. Romano, Paul K. Shriwise, Patrick C. Merson, Jacob S. Distributed, Parallel, and Cluster Computing Computational Physics Unstructured mesh tallies are a bottleneck in Monte Carlo neutral particle transport simulations of fusion reactors. This paper introduces the PUMI-Tally library that takes advantage of mesh adjacency information to accelerate these tallies on CPUs and GPUs. For a fixed source simulation using track-length tallies, we achieved a speed-up of 19.7X on an NVIDIA A100, and 9.2X using OpenMP on 128 threads of two AMD EPYC 7763 CPUs on NERSC Perlmutter. On the Empire AI alpha system, we achieved a speed-up of 20X using an NVIDIA H100 and 96 threads of an Intel Xenon 8568Y+. Our method showed better scaling with number of particles and number of elements. Additionally, we observed a 199X reduction in the number of allocations during initialization and the first three iterations, with a similar overall memory consumption. And, our hybrid CPU/GPU method demonstrated a 6.69X improvement in the energy consumption over the current approach. |
| title | GPU Acceleration of Monte Carlo Tallies on Unstructured Meshes in OpenMC with PUMI-Tally |
| topic | Distributed, Parallel, and Cluster Computing Computational Physics |
| url | https://arxiv.org/abs/2504.19048 |