Exploiting task and data parallelism on a multicomputer
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
A parallel algorithm for polygon rasterization
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
The point in polygon problem for arbitrary polygons
Computational Geometry: Theory and Applications
Out-of-core sort-first parallel rendering for cluster-based tiled displays
Parallel Computing - Parallel graphics and visualisation
Distributed frameworks and parallel algorithms for processing large-scale geographic data
Parallel Computing - Special issue: High performance computing with geographical data
ACM Transactions on Graphics (TOG)
Extraction of drainage networks from large terrain datasets using high throughput computing
Computers & Geosciences
A new hierarchical triangle-based point-in-polygon data structure
Computers & Geosciences
International Journal of Geographical Information Science
A general parallelization strategy for random path based geostatistical simulation methods
Computers & Geosciences
A SIMD-efficient 14 instruction shader program for high-throughput microtriangle rasterization
The Visual Computer: International Journal of Computer Graphics
ManyLoDs: parallel many-view level-of-detail selection for real-time global illumination
EGSR'11 Proceedings of the Twenty-second Eurographics conference on Rendering
Implementation and performance optimization of a parallel contour line generation algorithm
Computers & Geosciences
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With the expansion of complex geographic calculations and the increase of spatial data types involved in the spatial analysis of large areas, the need becomes urgent for fast rasterization of massive multi-source geographic vector data. A parallel scanline algorithm is proposed for rapid rasterization. It provides a systematic solution to solve the complicated situation in parallel processing (cross-processor boundaries, common boundaries, and tiny polygons), thus ensuring the accuracy of the parallel scanline algorithm. The relationship of parallel speedup with the number of processors, the data partition pattern, and the raster grid size is discussed. Massive vector geographic data (approximately 0.7 million polygons) used in the experiment were effectively processed, thereby dramatically reducing the processing time and getting good speedup.