Two-level locality-aware parallel Delaunay image-to-mesh conversion

In this paper, we propose a three dimensional two-level Locality-Aware Parallel Delaunay image-to-mesh conversion algorithm (LAPD). The algorithm exploits two levels of parallelism at different granularities: coarse-grain parallelism at the region level (which is mapped to a node with multiple cores) and medium-grain parallelism at the cavity level (which is mapped to a single core). We employ a data locality-aware mesh refinement process to reduce the latency caused by the remote memory access. We evaluated LAPD on Blacklight, a cachecoherent NUMA distributed shared memory (DSM) machine in the Pittsburgh Supercomputing Center, and observed a weak scaling efficiency of almost 70% for roughly 200 cores, compared to only 30% for the previous algorithm, Parallel Optimistic Mesh Generation algorithm (PODM).