Title

Performance of an Astrophysical Radiation Hydrodynamics Code under Scalable Vector Extension Optimization

Document Type

Article

Publication Date

2022

Abstract

We present results of a performance study of an astrophysical radiation hydrodynamics code, V2D, on the Armbased 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 analysis 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.

Comments

EAHPC-2022 - Embracing Arm for High Performance Computing Workshop, An IEEE Cluster 2022 Workshop

https://github.com/arm-hpc-user-group/eahpc-2022/blob/11d031b3e9ccb6c4a25fff768fc332098b7ba6fd/presentations/03_smolarski_astrophysical_radiation_sve_eahpc22.pdf

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