2025/10/27 by Hirashima, Keiya, Fujii, Michiko S., Saitoh, Takayuki R. +9 · 1 citation
#Astrophysics of Galaxies (astro-ph.GA) #Computational Physics (physics.comp-ph) #Distributed #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Parallel #and Cluster Computing (cs.DC)
paper · doi:10.48550/arxiv.2510.23330
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.