The First Star-by-star $N$-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model
This enables the first star-by-star simulation of the Milky Way, addressing a major bottleneck in computational astrophysics for researchers studying galaxy formation.
The authors tackled the challenge of simulating the Milky Way Galaxy at star-by-star resolution by developing a novel integration scheme that combines N-body/hydrodynamics simulations with a machine learning surrogate model to bypass short timesteps from supernova explosions, achieving a simulation with 300 billion particles using 148,900 nodes and scaling over 10^4 CPU cores.
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.