2025-11-21 理化学研究所,,神戸大学,筑波大学
開発した銀河シミュレーションコード「ASURA-FDPS-ML」の概念図
＜関連情報＞
- https://www.riken.jp/press/2025/20251117_2/index.html
- https://dl.acm.org/doi/10.1145/3712285.3759866
代替モデルを用いた銀河結合の初めての星別$N$体/流体力学シミュレーション The First Star-by-star $N$-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model
Keiya Hirashima, Michiko S Fujii, Takayuki R Saitoh, Naoto Harada, Kentaro Nomura, Kohji Yoshikawa, + 6
SC ’25: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis Published: 15 November 2025
DOI:https://doi.org/10.1145/3712285.3759866
Abstract
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 104 CPU cores, an upper limit in the current state-of-the-art simulations using both A64FX and X86-64 processors and NVIDIA CUDA GPUs.
