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

Abstract

The Locality-Aware Parallel Delaunay (LAPD) method exploits concurrency at two different levels of granularities (data region and cavity) and employs data partitioning to improve data locality; as a result LAPD scales well on Distributed Shared Memory computers with about 200 cores. 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 cache-coherent 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). To the best of our knowledge, LAPD exhibits the best scalability for parallel Delaunay mesh generation algorithms running on NUMA DSM supercomputers.