SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Multidimensional access methods
ACM Computing Surveys (CSUR)
A greedy algorithm for bulk loading R-trees
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Performance of Data-Parallel Spatial Operations
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Parallel R-Tree Search Algorithm on DSVM
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
Master-Client R-Trees: A New Parallel R-Tree Architecture
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Parallel bulk-loading of spatial data
Parallel Computing - Special issue: High performance computing with geographical data
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Execution time analysis of a top-down R-tree construction algorithm
Information Processing Letters
Parallel quadtree coding of large-scale raster geospatial data on GPGPUs
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Speeding up large-scale point-in-polygon test based spatial join on GPUs
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
Sort-based parallel loading of R-trees
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
Parallel multi-dimensional range query processing with R-trees on GPU
Journal of Parallel and Distributed Computing
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R-Trees are popular spatial indexing techniques that have been widely adopted in many geospatial applications. As commodity GPUs (Graphics Processing Units) are increasingly becoming available on personal workstations and cluster computers, there are considerable research interests in applying the massive data parallel GPGPU (General Purpose computing on GPUs) technologies to index and query large-scale geospatial data on GPUs using R-Trees. In this study, we aim at evaluating the potentials of accelerating both R-Tree bulk loading and spatial window query processing on GPUs using R-Trees. In addition to designing an efficient data layout schema for R-Trees on GPUs, we have implemented several parallel spatial window query processing techniques on GPUs using both dynamically generated R-Trees constructed on CPUs and bulk loaded R-Trees constructed on GPUs. Extensive experiments using both synthetic and real-world datasets have shown that our GPU based parallel query processing techniques using R-Trees can achieve about 10X speedups on average over 8-core CPU parallel implementations by effectively utilizing large numbers of processors and high memory bandwidth on GPUs.