  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.
