Fast computation of generalized Voronoi diagrams using graphics hardware
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
ACM Computing Surveys (CSUR)
Parallaxis-III: Architecture-Independent Data Parallel Processing
IEEE Transactions on Software Engineering - Special issue on architecture-independent languages and software tools for parallel processing
Parallel Algorithms to Find the Voronoi Diagram and the Order-k Voronoi Diagram
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Parallel Solutions to Geometric Problems on the Scan Model of Computation
Parallel Solutions to Geometric Problems on the Scan Model of Computation
Concepts and Techniques of Geographic Information Systems (2nd Edition) (Ph Series in Geographic Information Science)
Region-restricted clustering for geographic data mining
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
GPU computing with NVIDIA CUDA
ACM SIGGRAPH 2007 courses
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This paper presents two discrete computational geometry algorithms designed for execution on Graphics Processing Units (GPUs). The algorithms are parallelized versions of sequential algorithms intended for application in geographical data mining. The first algorithm finds clusters of m points, called m-clusters, in the rasterized plane. The second algorithm complements the first by identifying outliers, those points which are not members of any m-clusters. The use of a raster representation of coordinates provides an ideal data stream environment for efficient GPU utilization. The parallel algorithms have low memory demands, and require only a limited amount of inter-process communication. Initial performance analysis indicates the algorithms are scalable, both in problem size and in the number of seeds, and significantly outperform commercial implementations.