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

  title={Speeding up large-scale point-in-polygon test based spatial join on GPUs},
  author={Jianting Zhang and Simin You},
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… CONTINUE READING
Highly Cited
This paper has 68 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 40 extracted citations

Large-scale spatial data processing on GPUs and GPU-accelerated clusters

SIGSPATIAL Special • 2014
View 6 Excerpts
Highly Influenced

Parallel Selectivity Estimation for Optimizing Multidimensional Spatial Join Processing on GPUs

2017 IEEE 33rd International Conference on Data Engineering (ICDE) • 2017
View 4 Excerpts

68 Citations

Citations per Year
Semantic Scholar estimates that this publication has 68 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-2 of 2 references

Similar Papers

Loading similar papers…