Investigate the efficiency of incompressible flow simulations on CPUs and GPUs with BSAMR

TL;DR

This paper evaluates the computational efficiency of block-structured adaptive mesh refinement (BSAMR) in incompressible flow simulations on CPUs and GPUs, analyzing parameter impacts and proposing a new projection skipping method.

Contribution

It provides a comprehensive parametric study of BSAMR efficiency, introduces a novel projection skipping technique, and offers practical guidelines for optimizing incompressible flow simulations.

Findings

Optimal block size and refinement frequency depend on hardware and flow complexity.

Projection skipping can significantly reduce computation without sacrificing accuracy.

Empirical guidelines improve BSAMR performance on CPUs and GPUs.

Abstract

Adaptive mesh refinement (AMR) is a classical technique about local refinement in space where needed, thus effectively reducing computational costs for HPC-based physics simulations. Although AMR has been used for many years, little reproducible research discusses the impact of software-based parameters on block-structured AMR (BSAMR) efficiency and how to choose them. This article primarily does parametric studies to investigate the computational efficiency of incompressible flows on a block-structured adaptive mesh. The parameters include refining block size, refining frequency, maximum level, and cycling method. A new projection skipping (PS) method is proposed, which brings insights about when and where the projections on coarser levels are safe to be omitted. We conduct extensive tests on different CPUs/GPUs for various 2D/3D incompressible flow cases, including bubble, RT instability, Taylor Green vortex, etc. Several valuable empirical conclusions are obtained to help guide simulations with BSAMR. Codes and all profiling data are available on GitHub.