Efficient parallel extraction of crack-free isosurfaces from adaptive mesh refinement (AMR) data

We present a novel extraction scheme for crack-free isosurfaces from adaptive mesh refinement (AMR) data that builds on prior work utilizing dual grids and filling resulting gaps with stitch cells. We use a case-table-based approach to simplify the implementation of stitch cell generation. The most significant benefit of our new approach is that it uses ghost data to handle parallel isosurface extraction efficiently. We further present the results of applying this method to large scale data sets and analyze its computation time on parallel high-performance computing (HPC) platforms.