Accelerating Particle-in-Cell Monte Carlo Simulations with MPI, OpenMP/OpenACC and Asynchronous Multi-GPU Programming
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2024
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| author | Williams, Jeremy J. Liu, Felix Trilaksono, Jordy Tskhakaya, David Costea, Stefan Kos, Leon Podolnik, Ales Hromadka, Jakub Hegde, Pratibha Garcia-Gasulla, Marta Seitz, Valentin Jenko, Frank Laure, Erwin Markidis, Stefano |
| author_facet | Williams, Jeremy J. Liu, Felix Trilaksono, Jordy Tskhakaya, David Costea, Stefan Kos, Leon Podolnik, Ales Hromadka, Jakub Hegde, Pratibha Garcia-Gasulla, Marta Seitz, Valentin Jenko, Frank Laure, Erwin Markidis, Stefano |
| contents | As fusion energy devices advance, plasma simulations are crucial for reactor design. Our work extends BIT1 hybrid parallelization by integrating MPI with OpenMP and OpenACC, focusing on asynchronous multi-GPU programming. Results show significant performance gains: 16 MPI ranks plus OpenMP threads reduced runtime by 53% on a petascale EuroHPC supercomputer, while OpenACC multicore achieved a 58% reduction. At 64 MPI ranks, OpenACC outperformed OpenMP, improving the particle mover function by 24%. On MareNostrum 5, OpenACC async(n) delivered strong performance, but OpenMP asynchronous multi-GPU approach proved more effective at extreme scaling, maintaining efficiency up to 400 GPUs. Speedup and parallel efficiency (PE) studies revealed OpenMP asynchronous multi-GPU achieving 8.77x speedup (54.81% PE), surpassing OpenACC (8.14x speedup, 50.87% PE). While PE declined at high node counts due to communication overhead, asynchronous execution mitigated scalability bottlenecks. OpenMP nowait and depend clauses improved GPU performance via efficient data transfer and task management. Using NVIDIA Nsight tools, we confirmed BIT1 efficiency for large-scale plasma simulations. OpenMP asynchronous multi-GPU implementation delivered exceptional performance in portability, high throughput, and GPU utilization, positioning BIT1 for exascale supercomputing and advancing fusion energy research. MareNostrum 5 brings us closer to achieving exascale performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10270 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Particle-in-Cell Monte Carlo Simulations with MPI, OpenMP/OpenACC and Asynchronous Multi-GPU Programming Williams, Jeremy J. Liu, Felix Trilaksono, Jordy Tskhakaya, David Costea, Stefan Kos, Leon Podolnik, Ales Hromadka, Jakub Hegde, Pratibha Garcia-Gasulla, Marta Seitz, Valentin Jenko, Frank Laure, Erwin Markidis, Stefano Distributed, Parallel, and Cluster Computing Performance Computational Physics As fusion energy devices advance, plasma simulations are crucial for reactor design. Our work extends BIT1 hybrid parallelization by integrating MPI with OpenMP and OpenACC, focusing on asynchronous multi-GPU programming. Results show significant performance gains: 16 MPI ranks plus OpenMP threads reduced runtime by 53% on a petascale EuroHPC supercomputer, while OpenACC multicore achieved a 58% reduction. At 64 MPI ranks, OpenACC outperformed OpenMP, improving the particle mover function by 24%. On MareNostrum 5, OpenACC async(n) delivered strong performance, but OpenMP asynchronous multi-GPU approach proved more effective at extreme scaling, maintaining efficiency up to 400 GPUs. Speedup and parallel efficiency (PE) studies revealed OpenMP asynchronous multi-GPU achieving 8.77x speedup (54.81% PE), surpassing OpenACC (8.14x speedup, 50.87% PE). While PE declined at high node counts due to communication overhead, asynchronous execution mitigated scalability bottlenecks. OpenMP nowait and depend clauses improved GPU performance via efficient data transfer and task management. Using NVIDIA Nsight tools, we confirmed BIT1 efficiency for large-scale plasma simulations. OpenMP asynchronous multi-GPU implementation delivered exceptional performance in portability, high throughput, and GPU utilization, positioning BIT1 for exascale supercomputing and advancing fusion energy research. MareNostrum 5 brings us closer to achieving exascale performance. |
| title | Accelerating Particle-in-Cell Monte Carlo Simulations with MPI, OpenMP/OpenACC and Asynchronous Multi-GPU Programming |
| topic | Distributed, Parallel, and Cluster Computing Performance Computational Physics |
| url | https://arxiv.org/abs/2404.10270 |