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...

Full description

Saved in:
Bibliographic Details
Published in:2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid) pp. 579 - 591
Main Authors: Ashan, M. K. Buddhi, Puri, Satish, Prasad, Sushil K.
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2023
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
DOI:10.1109/CCGrid57682.2023.00060