Scalable 3D hybrid parallel Delaunay image-to-mesh conversion algorithm for distributed shared memory architectures

Abstract

In this paper, we present a scalable three-dimensional hybrid parallel Delaunay image-to-mesh conversion algorithm (PDR.PODM) for distributed shared memory architectures. PDR.PODM is able to explore parallelism early in the mesh generation process thanks to the aggressive speculative approach employed by the Parallel Optimistic Delaunay Mesh generation algorithm (PODM). In addition, it decreases the communication overhead and improves data locality by making use of a data partitioning scheme offered by the Parallel Delaunay Refinement algorithm (PDR). PDR.PODM supports fully functional volume grading by creating elements with varying size. Small elements are created near boundary or inside the critical regions in order to capture the fine features while big elements are created in the rest of the mesh. We tested PDR.PODM on Blacklight, a distributed shared memory (DSM) machine in Pittsburgh Supercomputing Center. For the uniform mesh generation, we observed a weak scaling speedup of 163.8 and above for up to 256 cores as opposed to PODM whose weak scaling speedup is only 44.7 on 256 cores. PDR.PODM scales well on uniform refinement cases running on DSM supercomputers. The end result is that PDR.PODM can generate 18 million elements per second as opposed to 14 million per second in our earlier work. The varying size version sharply reduces the number of elements compared to the uniform version and thus reduces the time to generate the mesh while keeping the same fidelity.