GCMF: an efficient end-to-end spatial join system over large polygonal datasets on GPGPU platform

Abstract

Given two layers of large polygonal datasets, detecting those pairs of cross-layer polygons which satisfy a join predicate, such as intersection or contain, is one of the most computationally intensive primitive operations in the spatial domain applications. In this work, we introduce GCMF, an end-to-end software system, that is able to handle spatial join (with ST_Intersect operation) over non-indexed polygonal datasets with over 3 GB file size comprising more than 600, 000 polygons on a single GPU within less than 8 sec by applying innovative filter and refinement techniques. GCMF performs a two-step filtering phase. 1) A sort-based Minimum Bounding Rectangle (MBR) filtering step detects potentially overlapping polygon pairs up to 20 times faster than the optimized GEOS library routine. 2) A linear time Common MBR filtering step (based on the overlapping area of two given MBRs) that not only eliminates two-third of the candidate polygon pairs but also reduces the number of edges to be considered in the refinement phase by 40-fold on an average based on our experimental results with real datasets. Furthermore, for the refinement phase, GCMF implements a load-balanced parallel point-in-polygon and edge-intersection tests over GPU. Our experimental results with three different real datasets show up to 39-fold end-to- end speedup versus optimized sequential routines of GEOS C++ library as well as PostgreSQL spatial database with PostGIS.