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Parallel Primitives-Based Spatial Join of Geospatial Data on GPGPUs

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Modern Accelerator Technologies for Geographic Information Science

Abstract

Modern GPU architectures closely resemble supercomputers. Commodity GPUs that have already been integrated with personal and cluster computers can be used to boost the performance of spatial databases and GIS. In this study, we report our preliminary work on designing and implementing a spatial join algorithm on GPUs by using generic parallel primitives that have been well understood and efficiently implemented in many parallel libraries. In addition to help understand the inherent data parallelisms in spatial join operations, our experiments have shown that the reference implementation, which represents a tradeoff between code efficiency and code complexity, is able to achieve a 6.7× speedup when compared to an optimized CPU serial implementation. The result is encouraging in the sense that native implementation of spatial joins directly on top of GPU accelerators can potentially achieve much higher speedups for spatial joins which are fundamental to spatial databases and vector GIS. The implementations of parallel spatial algorithms on top of generic parallel primitives can be an important first step towards designing and developing high-performance spatial-specific parallel primitives to make it easier to build parallel spatial databases and GIS.

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Notes

  1. 1.

    http://en.wikipedia.org/wiki/GPGPU

  2. 2.

    http://code.google.com/p/thrust/

  3. 3.

    http://developer.nvidia.com/cuda-toolkit-40

  4. 4.

    http://www.nyc.gov/html/dcp/html/bytes/applbyte.shtml

  5. 5.

    http://libspatialindex.github.com/

  6. 6.

    http://www.gdal.org/

  7. 7.

    http://www.opengeospatial.org/standards/sfs

  8. 8.

    http://en.wikipedia.org/wiki/Thinking_Machines_Corporation

  9. 9.

    http://code.google.com/p/cudpp/

  10. 10.

    http://geoteci.engr.ccny.cuny.edu/primquad/primquad_draft.pdf (pages 4–5).

  11. 11.

    http://geoteci.engr.ccny.cuny.edu/primspjion/primspjoin.htm

  12. 12.

    http://www.nvidia.com/object/product-quadro-6000-us.html

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Acknowledgment

This research was supported partially by the PSC-CUNY grant 65692-00 43.

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Authors and Affiliations

  1. Department of Computer Science, The City College of New York, 138 Street at Convent Avenue, NAC 8/206, New York, NY, 10031, USA

    Jianting Zhang

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  1. Jianting Zhang
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Correspondence to Jianting Zhang .

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Editors and Affiliations

  1. Department of Geosciences, University of Arkansas, Fayetteville, Arkansas, USA

    Xuan Shi

  2. Department of Electrical and Computer Engineering, University of Illinois, Urbana, Illinois, USA

    Volodymyr Kindratenko

  3. Department of Geography and GeoInformation Sciences, George Mason University, Fairfax, Virginia, USA

    Chaowei Yang

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Zhang, J. (2013). Parallel Primitives-Based Spatial Join of Geospatial Data on GPGPUs. In: Shi, X., Kindratenko, V., Yang, C. (eds) Modern Accelerator Technologies for Geographic Information Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-8745-6_5

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  • DOI: https://doi.org/10.1007/978-1-4614-8745-6_5

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4614-8744-9

  • Online ISBN: 978-1-4614-8745-6

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