Multidimensional access methods
ACM Computing Surveys (CSUR)
Clone join and shadow join: two parallel spatial join algorithms
Proceedings of the 8th ACM international symposium on Advances in geographic information systems
A cell-based point-in-polygon algorithm suitable for large sets of points
Computers & Geosciences
Data Partitioning for Parallel Spatial Join Processing
Geoinformatica
Parallel Processing of Spatial Joins Using R-trees
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Parallel Spatial Joins Using Grid Files
ICPADS '00 Proceedings of the Seventh International Conference on Parallel and Distributed Systems
Hardware acceleration for spatial selections and joins
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
A Non-Blocking Parallel Spatial Join Algorithm
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
ACM Transactions on Database Systems (TODS)
In-memory grid files on graphics processors
DaMoN '07 Proceedings of the 3rd international workshop on Data management on new hardware
A Fast Similarity Join Algorithm Using Graphics Processing Units
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Relational query coprocessing on graphics processors
ACM Transactions on Database Systems (TODS)
Accelerating SQL database operations on a GPU with CUDA
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Towards personal high-performance geospatial computing (HPC-G): perspectives and a case study
Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems
Accelerating CUDA graph algorithms at maximum warp
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Parallel data processing with MapReduce: a survey
ACM SIGMOD Record
Efficient parallel kNN joins for large data in MapReduce
Proceedings of the 15th International Conference on Extending Database Technology
Parallel spatial query processing on GPUs using R-trees
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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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.