Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914710616539136 |
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| author | Fridman, Yehonatan Tamir, Guy Steinitz, Uri Oren, Gal |
| author_facet | Fridman, Yehonatan Tamir, Guy Steinitz, Uri Oren, Gal |
| contents | Monte Carlo (MC) simulations play a pivotal role in diverse scientific and engineering domains, with applications ranging from nuclear physics to materials science. Harnessing the computational power of high-performance computing (HPC) systems, especially Graphics Processing Units (GPUs), has become essential for accelerating MC simulations. This paper focuses on the adaptation and optimization of the OpenMC neutron and photon transport Monte Carlo code for Intel GPUs, specifically the Intel Data Center Max 1100 GPU (codename Ponte Vecchio, PVC), through distributed OpenMP offloading. Building upon prior work by Tramm J.R., et al. (2022), which laid the groundwork for GPU adaptation, our study meticulously extends the OpenMC code's capabilities to Intel GPUs. We present a comprehensive benchmarking and scaling analysis, comparing performance on Intel MAX GPUs to state-of-the-art CPU execution (Intel Xeon Platinum 8480+ Processor, codename 4th generation Sapphire Rapids). The results demonstrate a remarkable acceleration factor compared to CPU execution, showcasing the GPU-adapted code's superiority over its CPU counterpart as computational load increases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02735 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators Fridman, Yehonatan Tamir, Guy Steinitz, Uri Oren, Gal Distributed, Parallel, and Cluster Computing Monte Carlo (MC) simulations play a pivotal role in diverse scientific and engineering domains, with applications ranging from nuclear physics to materials science. Harnessing the computational power of high-performance computing (HPC) systems, especially Graphics Processing Units (GPUs), has become essential for accelerating MC simulations. This paper focuses on the adaptation and optimization of the OpenMC neutron and photon transport Monte Carlo code for Intel GPUs, specifically the Intel Data Center Max 1100 GPU (codename Ponte Vecchio, PVC), through distributed OpenMP offloading. Building upon prior work by Tramm J.R., et al. (2022), which laid the groundwork for GPU adaptation, our study meticulously extends the OpenMC code's capabilities to Intel GPUs. We present a comprehensive benchmarking and scaling analysis, comparing performance on Intel MAX GPUs to state-of-the-art CPU execution (Intel Xeon Platinum 8480+ Processor, codename 4th generation Sapphire Rapids). The results demonstrate a remarkable acceleration factor compared to CPU execution, showcasing the GPU-adapted code's superiority over its CPU counterpart as computational load increases. |
| title | Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2403.02735 |