|Title||High-Performance Computation of Distributed-Memory Parallel 3D Voronoi and Delaunay Tessellation |
|Publication Type||Conference Paper |
|Year of Publication||2014 |
|Authors||Peterka, T, Morozov, D, Phillips, CL |
|Conference Name||SC'14 |
|Conference Location||New Orleans, LA |
|Other Numbers||ANL/MCS-P5154-0614 |
|Abstract||Computing a Voronoi or Delaunay tessellation from a set of points is a core part of the analysis of many simulated and measured datasets: N-body simulations, molecular dynamics codes, and LIDAR point clouds are just a few examples. Such computational geometry methods are common in data analysis and visualization; but as the scale of simulations and observations surpasses billions of particles, the existing serial and shared-memory algorithms no longer suffice. A distributed-memory scalable parallel algorithm is the only feasible approach. The primary contribution of this paper is a new parallel Delaunay and Voronoi tessellation algorithm that automatically determines which neighbor points need to be exchanged among the subdomains of a spatial decomposition. Other contributions include the addition of periodic and wall boundary conditions, comparison of parallelization based on two popular serial libraries, and application to numerous science datasets.