Fast Parallel Triangulation Algorithm of Large Data Sets in E

A triangulation of points in , or a tetrahedronization of points in , is used in many applications. It is not necessary to fulfill the Delaunay criteria in all cases. For large data (more then 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 or , 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.