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  1. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial '13)
  2. Parallel spatial query processing on GPUs using R-trees
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A cluster-based morphological filter for geospatial data analysis
The price of generality in spatial indexing
When big data meets big smog: a big spatio-temporal data framework for China severe smog analysis
Parallel spatial query processing on GPUs using R-trees
Breaking the big data barrier by enhancing on-board sensor flexibility
Discovering persistent change windows in spatiotemporal datasets: a summary of results
$P^{2}EST:$ parallelization philosophies for evaluating spatio-temporal queries
Algorithms for fundamental spatial aggregate operations over regions
GPGPU-accelerated interesting interval discovery and other computations on GeoSpatial datasets: a summary of results
Object based image classification: state of the art and computational challenges

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Parallel spatial query processing on GPUs using R-trees

Content Provider ACM Digital Library
Author You, Simin Zhang, Jianting Gruenwald, Le
Abstract 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.
Starting Page 23
Ending Page 31
Page Count 9
File Format PDF
ISBN 9781450325349
DOI 10.1145/2534921.2534949
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-11-04
Publisher Place New York
Access Restriction Subscribed
Subject Keyword R-tree Spatial indexing Gpu
Content Type Text
Resource Type Article
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