Probabilistic methods for centroidal Voronoi tessellations and their parallel implementations

Abstract

Centroidal Voronoi tessellations (CVTs) are Voronoi tessellations of a region such that the generating points of the tessellations are also the centroids of the corresponding Voronoi cells. In this paper, some probabilistic methods for determining CVTs and their parallel implementations on distributed memory systems are presented. By using multi-sampling in a new probabilistic algorithm we introduce, more accurate and efficient approximations of CVTs are obtained without the need to explicit construct Voronoi diagrams. The new algorithm lends itself well to parallelization, i.e., near prefect linear speed up in the number of processors is achieved. The results of computational experiments performed on a CRAY T3E−600 system are provided which illustrate the superior sequential and parallel performance of the new algorithm when compared to existing algorithms. In particular, for the same amount of work, the new algorithms produce significantly more accurate CVTs.