Computational Statistics

, Volume 26, Issue 2, pp 219–239 | Cite as

Hands-on tutorial for parallel computing with R

  • Manuel J. A. EugsterEmail author
  • Jochen Knaus
  • Christine Porzelius
  • Markus Schmidberger
  • Esmeralda Vicedo
Original Paper


Due to the increasing availability of powerful hardware resources, parallel computing is becoming an important issue, as a noticeable speedup may be achieved. The statistical programming language R allows for parallel computing on computer clusters as well as multicore systems through several packages. This tutorial gives a short, practical overview of four, in view of the authors, important packages for parallel computing in R, namely multicore, snow, snowfall and nws. First, the general principle of parallelizing simple tasks is briefly illustrated based on a statistical cross-validation example. Afterwards, the usage of each of the introduced packages is being demonstrated on the example. Furthermore, we address some specific features of the packages and provide guidance for selecting an adequate package for the computing environment at hand.


Parallel computing Multicore Snow Snowfall nws 


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Manuel J. A. Eugster
    • 1
    Email author
  • Jochen Knaus
    • 2
  • Christine Porzelius
    • 2
  • Markus Schmidberger
    • 3
  • Esmeralda Vicedo
    • 3
  1. 1.Department of StatisticsLudwig-Maximilians-Universität MünchenMunichGermany
  2. 2.Institute of Medical Biometry and Medical InformaticsUniversity Medical Center FreiburgFreiburgGermany
  3. 3.Division of Biometrics and BioinformaticsLudwig-Maximilians-Universität MünchenMunichGermany

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