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Fast parallel triangulation algorithm of large data sets in e2 and e3 for in-core and out-core memory processing.
| Content Provider | CiteSeerX |
|---|---|
| Author | Smolik, Michal Skala, Vaclav |
| Abstract | Abstract. 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 5 ∙ 10 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. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Large Data Set Fast Parallel Triangulation Algorithm Out-core Memory Processing Gpu Triangulation Delaunay Triangulation Tetrahedralization Many Application Delaunay Criterion New Approach Triangulation Tetrahedralization Large Data Parallel Method Effective Parallel Cpu Time Complexity Experimental Result |
| Content Type | Text |
| Resource Type | Article |
