Document Type
Conference Proceeding
Publication Date
11-2020
Publisher
Association for Computing Machinery
Source Publication
SIGSPATIAL '20: Proceedings of the 28th International Conference on Advances in Geographic Information Systems
Source ISSN
978-1-4503-8019-5
Original Item ID
DOI: 10.1145/3397536.3422264
Abstract
Geometric intersection algorithms are fundamental in spatial analysis in Geographic Information System (GIS). Applying high performance computing to perform geometric intersection on huge amount of spatial data to get real-time results is necessary. Given two input geometries (polygon or polyline) of a candidate pair, we introduce a new two-step geospatial filter that first creates sketches of the geometries and uses it to detect workload and then refines the sketches by the common areas of sketches to decrease the overall computations in the refine phase. We call this filter PolySketch-based CMBR (PSCMBR) filter. We show the application of this filter in speeding-up line segment intersections (LSI) reporting task that is a basic computation in a variety of geospatial applications like polygon overlay and spatial join.
We also developed a parallel PolySketch-based PNP filter to perform PNP tests on GPU which reduces computational workload in PNP tests. Finally, we integrated these new filters to the hierarchical filter and refinement (HiFiRe) system to solve geometric intersection problem. We have implemented the new filter and refine system on GPU using CUDA. The new filters introduced in this paper reduce more computational workload when compared to existing filters. As a result, we get on average 7.96X speedup compared to our prior version of HiFiRe system.
Recommended Citation
Liu, Yiming and Puri, Satish, "Efficient Filters for Geometric Intersection Computations using GPU" (2020). Computer Science Faculty Research and Publications. 43.
https://epublications.marquette.edu/comp_fac/43
Comments
Published in SIGSPATIAL '20: Proceedings of the 28th International Conference on Advances in Geographic Information Systems. Seattle WA, November 3-6, 2020. DOI. © 2020 Association for Computing Machinery (ACM). Used with permission.