Communications of the ACM - Special issue on parallelism
Spatial query processing in an object-oriented database system
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Vector models for data-parallel computing
Vector models for data-parallel computing
Making B+- trees cache conscious in main memory
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Cache Conscious Indexing for Decision-Support in Main Memory
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
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
Speeding up large-scale geospatial polygon rasterization on GPGPUs
Proceedings of the ACM SIGSPATIAL Second International Workshop on High Performance and Distributed Geographic Information Systems
Accelerating batch processing of spatial raster analysis using GPU
Computers & Geosciences
CudaGIS: report on the design and realization of a massive data parallel GIS on GPUs
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
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Advances in geospatial technologies have generated large amounts of raster geospatial data. Massively parallel General Purpose Graphics Processing Unit (GPGPU) computing technologies have provided personal computers with tremendous computing capabilities. In this paper, we report our work on fast indexing of large-scale raster geospatial data using GPGPU computing. We have designed a cache conscious quadtree data structure (CCQ-Tree) that is suitable for GPU indexing. A set of algorithms have been developed and integrated to construct CCQ-Trees on GPUs by utilizing multiple pyramid data structures and Z-order based prefix sum. Experiments on multiple 4096*4096 blocks of a global precipitation raster data have shown that CCQ-Tree indexing using a 112-core Nvidia Quadro FX3700 GPU device reduces construction times from around 9.83 seconds to 0.42 seconds (23X speedup).