A Scalable Parallel Computing Framework for Large-Scale Astrophysical Fluid Dynamics Numerical Simulation - Kulikov, Igor; Chernykh, Igor; Tchernykh, Andrei (2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2019)
The numerical simulation of complex astrophysical problems requires high-performance computing due to the large size of the problems and variety of simulated physical processes. In this paper, we present a new framework for the numerical simulation of astrophysical fluid dynamics. It is based on the mechanisms of combining distributed and parallel computing techniques, advanced vectorization for KNL, and Skylake-SP CPU architectures. Our new HydroBox3D framework uses large 3D meshes to solve problems such as the dynamics of stars or galaxies. In our framework, we use computational nodes with a large amount of memory (RAM or Intel Optane in memory mode) for mesh processing and typical computational nodes for the numerical simulation of astrophysical problems. We use MPI both for send/receive operations between computational nodes and for sending processed data for calculations from data nodes. For optimization of calculations, memory, and CPU usage, we use data vectorization, FMA3, and AVX-512 instructions for Intel Xeon Phi 72XX and Intel Xeon Scalable processors. Benchmark results on different CPU and MIC devices show the effectiveness of the proposed solution.