Speeding up large-scale point-in-polygon test based spatial join on GPUs

Abstract

Point-in-Polygon (PIP) test is fundamental to spatial databases and GIS. Motivated by the slow response times in joining large-scale point locations with polygons using traditional spatial databases and GIS, we have designed and developed an end-to-end system completely on Graphics Processing Units (GPUs) to associate points with the polygons that they fall within by utilizing massively data parallel computing power of GPUs. The system includes an efficient module to generate point quadrants that have at most K points from large-scale unordered points, a simple grid-file based spatial filtering approach to associate point quadrants and polygons, and, a PIP test module to assign polygons to points in a GPU computing block using both the block and thread level parallelisms. Experiments on joining 170 million points with more than 40 thousand polygons have resulted in a runtime of 11.165 seconds on an Nvidia Quadro 6000 GPU device. In contrast, a baseline serial CPU implementation using state-of-the-art open source GIS packages required 15+ hours to complete. We further discuss several factors and parameters that may affect the system performance.