Efficient PRAM and Practical GPU Algorithms for Large Polygon Clipping with Degenerate Cases
Polygonal geometric operations are fundamental in domains such as Computer Graphics, Computer-Aided Design, and Geographic Information Systems. Handling degenerate cases in such operations is important when real-world spatial data are used. The popular Greiner-Hormann (GH) clipping algorithm does no...
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| Vydáno v: | 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid) s. 579 - 591 |
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01.05.2023
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| Abstract | Polygonal geometric operations are fundamental in domains such as Computer Graphics, Computer-Aided Design, and Geographic Information Systems. Handling degenerate cases in such operations is important when real-world spatial data are used. The popular Greiner-Hormann (GH) clipping algorithm does not handle such cases properly without perturbing vertices leading to inaccuracies and ambiguities. In this work, we parallelize the O (n 2 )-time general polygon clipping algorithm by Foster et al., which can handle degenerate cases without perturbation. Our CREW PRAM algorithm can perform clipping in O (log n) time using n + k number of processors with simple polygons, where n is the number of input edges and k is the number of edge intersections. For efficient GPU implementation, we employ three effective filters which have not been used in prior work on polygon clipping: 1) Common-minimum-bounding-rectangle filter, 2) Count-based filter, and 3) Line-segment-minimum-bounding-rectangle filter. They drastically reduce O( n 2 ) candidate edge pairs comparisons by 80% - 99%, leading to significantly faster parallel execution. In our experiments, C++ CUDA-based implementation yields up to 40X speedup over real-world datasets, processing two polygons with a total of 174K vertices on an Nvidia Quadro RTX 5000 GPU compared to the sequential Foster's algorithm running on an Intel Xeon Silver 4210R CPU. |
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| AbstractList | Polygonal geometric operations are fundamental in domains such as Computer Graphics, Computer-Aided Design, and Geographic Information Systems. Handling degenerate cases in such operations is important when real-world spatial data are used. The popular Greiner-Hormann (GH) clipping algorithm does not handle such cases properly without perturbing vertices leading to inaccuracies and ambiguities. In this work, we parallelize the O (n 2 )-time general polygon clipping algorithm by Foster et al., which can handle degenerate cases without perturbation. Our CREW PRAM algorithm can perform clipping in O (log n) time using n + k number of processors with simple polygons, where n is the number of input edges and k is the number of edge intersections. For efficient GPU implementation, we employ three effective filters which have not been used in prior work on polygon clipping: 1) Common-minimum-bounding-rectangle filter, 2) Count-based filter, and 3) Line-segment-minimum-bounding-rectangle filter. They drastically reduce O( n 2 ) candidate edge pairs comparisons by 80% - 99%, leading to significantly faster parallel execution. In our experiments, C++ CUDA-based implementation yields up to 40X speedup over real-world datasets, processing two polygons with a total of 174K vertices on an Nvidia Quadro RTX 5000 GPU compared to the sequential Foster's algorithm running on an Intel Xeon Silver 4210R CPU. |
| Author | Ashan, M. K. Buddhi Puri, Satish Prasad, Sushil K. |
| Author_xml | – sequence: 1 givenname: M. K. Buddhi surname: Ashan fullname: Ashan, M. K. Buddhi email: buddhiashan.mallikakankanamalage@utsa.edu organization: University of Texas at,Department of Computer Science,San Antonio – sequence: 2 givenname: Satish surname: Puri fullname: Puri, Satish email: satish.puri@marquette.edu organization: Marquette University,Department of Computer Science – sequence: 3 givenname: Sushil K. surname: Prasad fullname: Prasad, Sushil K. email: sushil.prasad@utsa.edu organization: University of Texas at,Department of Computer Science,San Antonio |
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| SubjectTerms | C++ languages Clustering algorithms degenerate intersections Filtering algorithms Foster et al. algorithm GPU algorithm Graphics processing units Greiner-Hormann algorithm Perturbation methods Phase change random access memory polygon clipping PRAM algorithm Silver |
| Title | Efficient PRAM and Practical GPU Algorithms for Large Polygon Clipping with Degenerate Cases |
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