Fast parallel triangulation algorithm of large data sets in E2 and E3 for in-core and out-core memory processing

Abstract

A triangulation of points in E2, or a tetrahedronization of points in E 3, is used in many applications. It is not necessary to fulfill the Delaunay criteria in all cases. For large data (more then 5.10 7 points), parallel methods are used for the purpose of decreasing time complexity. A new approach for fast and effective parallel CPU and GPU triangulation, or tetrahedronization, of large data sets in E 2 or E 3, is proposed in this paper. Experimental results show that the triangulation/tetrahedralization, is close to the Delaunay triangulation/ tetrahedralization. It also demonstrates the applicability of the method presented in applications. © 2014 Springer International Publishing.