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Exploiting Parallelization in Spatial Statistics: an Applied Survey using R

Author

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  • Bivand, Roger

    (Dept. of Economics, Norwegian School of Economics and Business Administration)

Abstract

Computing tasks may be parallelized top-down by splitting into per-node chunks when the tasks permit this kind of division, and particularly when there is little or no need for communication between the nodes. Another approach is to parallelize bottom-up, by the substitution of multi-threaded low-level functions for single-threaded ones in otherwise unchanged user-level functions. This survey examines the timings of typical spatial data analysis tasks across a range of data sizes and hardware under different combinations of these two approaches. Conclusions are drawn concerning choices of alternatives for parallelization, and attention is drawn to factors conditioning those choices.

Suggested Citation

  • Bivand, Roger, 2010. "Exploiting Parallelization in Spatial Statistics: an Applied Survey using R," Discussion Paper Series in Economics 25/2010, Norwegian School of Economics, Department of Economics.
    • Handle: RePEc:hhs:nhheco:2010_025
    as

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    References listed on IDEAS

    as
    1. Pierre L'Ecuyer & Richard Simard & E. Jack Chen & W. David Kelton, 2002. "An Object-Oriented Random-Number Package with Many Long Streams and Substreams," Operations Research, INFORMS, vol. 50(6), pages 1073-1075, December.
    2. Bivand, Roger & Müller, Werner G. & Reder, Markus, 2009. "Power calculations for global and local Moran's," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2859-2872, June.
    3. Schmidberger, Markus & Morgan, Martin & Eddelbuettel, Dirk & Yu, Hao & Tierney, Luke & Mansmann, Ulrich, 2009. "State of the Art in Parallel Computing with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i01).
    4. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Statistical software; Parallelization; Optimized linear algebra subroutines; Multicore processors; Spatial statistics.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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