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.

翻译：自适应网格加密（AMR）是一种经典的空间局部加密技术，可有效降低基于高性能计算的物理模拟计算成本。尽管AMR已应用多年，但关于软件参数对块结构自适应网格加密（BSAMR）效率的影响及参数选择方法的可复现研究仍较为缺乏。本文主要通过参数化研究，探讨块结构自适应网格下不可压缩流的计算效率。研究参数包括加密块尺寸、加密频率、最大层级及循环策略。我们提出了一种新的投影跳过（PS）方法，该方法揭示了在粗层级上何时及何处可以安全省略投影操作。通过在多种CPU/GPU平台上针对二维/三维不可压缩流算例（包括气泡、瑞利-泰勒不稳定性、泰勒-格林涡等）进行广泛测试，获得了若干有价值的经验性结论，可指导BSAMR模拟实践。所有代码及分析数据已在GitHub开源。