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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.23330 |
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| _version_ | 1866911234804154368 |
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| author | Hirashima, Keiya Fujii, Michiko S. Saitoh, Takayuki R. Harada, Naoto Nomura, Kentaro Yoshikawa, Kohji Hirai, Yutaka Asano, Tetsuro Moriwaki, Kana Iwasawa, Masaki Okamoto, Takashi Makino, Junichiro |
| author_facet | Hirashima, Keiya Fujii, Michiko S. Saitoh, Takayuki R. Harada, Naoto Nomura, Kentaro Yoshikawa, Kohji Hirai, Yutaka Asano, Tetsuro Moriwaki, Kana Iwasawa, Masaki Okamoto, Takashi Makino, Junichiro |
| contents | A major goal of computational astrophysics is to simulate the Milky Way Galaxy with sufficient resolution down to individual stars. However, the scaling fails due to some small-scale, short-timescale phenomena, such as supernova explosions. We have developed a novel integration scheme of $N$-body/hydrodynamics simulations working with machine learning. This approach bypasses the short timesteps caused by supernova explosions using a surrogate model, thereby improving scalability. With this method, we reached 300 billion particles using 148,900 nodes, equivalent to 7,147,200 CPU cores, breaking through the billion-particle barrier currently faced by state-of-the-art simulations. This resolution allows us to perform the first star-by-star galaxy simulation, which resolves individual stars in the Milky Way Galaxy. The performance scales over $10^4$ CPU cores, an upper limit in the current state-of-the-art simulations using both A64FX and X86-64 processors and NVIDIA CUDA GPUs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23330 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The First Star-by-star $N$-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model Hirashima, Keiya Fujii, Michiko S. Saitoh, Takayuki R. Harada, Naoto Nomura, Kentaro Yoshikawa, Kohji Hirai, Yutaka Asano, Tetsuro Moriwaki, Kana Iwasawa, Masaki Okamoto, Takashi Makino, Junichiro Astrophysics of Galaxies Distributed, Parallel, and Cluster Computing Machine Learning Computational Physics A major goal of computational astrophysics is to simulate the Milky Way Galaxy with sufficient resolution down to individual stars. However, the scaling fails due to some small-scale, short-timescale phenomena, such as supernova explosions. We have developed a novel integration scheme of $N$-body/hydrodynamics simulations working with machine learning. This approach bypasses the short timesteps caused by supernova explosions using a surrogate model, thereby improving scalability. With this method, we reached 300 billion particles using 148,900 nodes, equivalent to 7,147,200 CPU cores, breaking through the billion-particle barrier currently faced by state-of-the-art simulations. This resolution allows us to perform the first star-by-star galaxy simulation, which resolves individual stars in the Milky Way Galaxy. The performance scales over $10^4$ CPU cores, an upper limit in the current state-of-the-art simulations using both A64FX and X86-64 processors and NVIDIA CUDA GPUs. |
| title | The First Star-by-star $N$-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model |
| topic | Astrophysics of Galaxies Distributed, Parallel, and Cluster Computing Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2510.23330 |