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In this paper, we introduce our hierarchical filter and refinement technique that we have developed for parallel geometric intersection operations involving large polygons and polylines. The inputs are two layers of large polygonal datasets and the computations are spatial intersection on a pair of cross-layer polygons. These intersections are the compute-intensive spatial data analytic kernels in spatial join and map overlay computations. We have extended the classical filter and refine algorithms using PolySketch Filter to improve the performance of geospatial computations. In addition to filtering polygons by their Minimum Bounding Rectangle (MBR), our hierarchical approach explores further filtering using tiles (smaller MBRs) to increase the effectiveness of filtering and decrease the computational workload in the refinement phase. We have implemented this filter and refine system on CPU and GPU by using OpenMP and OpenACC. After using R-tree, on average, our filter technique can still discard 69% of polygon pairs which do not have segment intersection points. PolySketch filter reduces on average 99.77% of the workload of finding line segment intersections. PNP based task reduction and Striping algorithms filter out on average 95.84% of the workload of Point-in-Polygon tests. Our CPU-GPU system performs spatial join on two shapefiles, namely USA Water Bodies and USA Block Group Boundaries with 683K polygons in about 10 seconds using NVidia Titan V and Titan Xp GPU.
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OpenACC Based GPU Parallelization of Plane Sweep Algorithm for Geometric Intersection.
Line segment intersection is one of the elementary operations in computational geometry. Complex problems in Geographic Information Systems (GIS) like finding map overlays or spatial joins using polygonal data require solving segment intersections. Plane sweep paradigm is used for finding geometric intersection in an efficient manner. However, it is difficult to parallelize due to its in-order processing of spatial events. We present a new fine-grained parallel algorithm for geometric intersection and its CPU and GPU implementation using OpenMP and OpenACC. To the best of our knowledge, this is the first work demonstrating an effective parallelization of plane sweep on GPUs. We chose compiler directive based approach for implementation because of its simplicity to parallelize sequential code. Using Nvidia Tesla P100 GPU, our implementation achieves around 40X speedup for line segment intersection problem on 40K and 80K data sets compared to sequential CGAL library.
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- Award ID(s):
- 1756000
- PAR ID:
- 10088006
- Date Published:
- Journal Name:
- Chandrasekaran S., Juckeland G., Wienke S. (eds) Accelerator Programming Using Directives. WACCPD 2018. Lecture Notes in Computer Science, Springer, Cham
- Volume:
- 11381
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)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.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-12274-4
