We present results of a performance study of an astrophysical radiation hydrodynamics code, V2D, on the Arm-based A64FX processor developed by Fujitsu. The code solves sparse linear systems, a task for which the A64FX architecture should be well suited. We performed the performance analysis study on Ookami, an Apollo 80 platform utilizing the A64FX processor. We explored several compilers and performance anal-ysis packages and found the code did not perform as expected under scalable vector extension optimization, suggesting that a “deeper dive” into analyzing the code is worthwhile. However, a simple driver program that exercised basic sparse linear algebra routines used by V2D did show significant speedup with the use of the scalable vector extension optimization. We present the initial results from the study which used V2D on a relatively simple test problem that emphasized the repeated solution of sparse linear systems.

We discuss the high performance computing issues involved in the numerical simulation of binary neutron star mergers and supernovae. These phenomena, which are of great interest to astronomers and physicists, can only be described by modeling the gravitational field of the objects along with the flow of matter and radiation in a self consistent manner. In turn, such models require the solution of the gravitational field equations, Eulerian hydrodynamic equations, and radiation transport equations. This necessitates the use of scalable, high performance computing assets to conduct the simulations. We discuss some of the parallel computing aspects of this challenging task in this paper.