Hands-on tutorial for parallel computing with R

Hands-on tutorial for parallel computing with R 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Statistics Springer Journals

Hands-on tutorial for parallel computing with R

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Publisher
Springer Journals
Copyright
Copyright © 2010 by Springer-Verlag
Subject
Statistics; Statistics, general; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Economic Theory/Quantitative Economics/Mathematical Methods
ISSN
0943-4062
eISSN
1613-9658
D.O.I.
10.1007/s00180-010-0206-4
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Computational StatisticsSpringer Journals

Published: Jul 17, 2010

References

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