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State of the Art in Parallel Computing with R

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  • Schmidberger, Markus
  • Morgan, Martin
  • Eddelbuettel, Dirk
  • Yu, Hao
  • Tierney, Luke
  • Mansmann, Ulrich

Abstract

R is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing. This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance. Two packages (snow, Rmpi) stand out as particularly suited to general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems five different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix.

Suggested Citation

  • 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).
    • Handle: RePEc:jss:jstsof:v:031:i01
    DOI: http://hdl.handle.net/10.18637/jss.v031.i01
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    Cited by:

    1. repec:jss:jstsof:37:i03 is not listed on IDEAS
    2. Andrew Karl & Randy Eubank & Jelena Milovanovic & Mark Reiser & Dennis Young, 2014. "Using RngStreams for parallel random number generation in C++ and R," Computational Statistics, Springer, vol. 29(5), pages 1301-1320, October.
    3. Manuel Eugster & Jochen Knaus & Christine Porzelius & Markus Schmidberger & Esmeralda Vicedo, 2011. "Hands-on tutorial for parallel computing with R," Computational Statistics, Springer, vol. 26(2), pages 219-239, June.
    4. Philippe Pébay & Timothy B. Terriberry & Hemanth Kolla & Janine Bennett, 2016. "Numerically stable, scalable formulas for parallel and online computation of higher-order multivariate central moments with arbitrary weights," Computational Statistics, Springer, vol. 31(4), pages 1305-1325, December.
    5. Tomasz Górecki & Łukasz Smaga, 2019. "fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data," Computational Statistics, Springer, vol. 34(2), pages 571-597, June.
    6. repec:jss:jstsof:35:i03 is not listed on IDEAS
    7. 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.
    8. repec:jss:jstsof:39:c01 is not listed on IDEAS

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