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State of the Art in Parallel Computing with R
Journal article   Open access

State of the Art in Parallel Computing with R

Markus Schmidberger, Martin Morgan, Dirk Eddelbuettel, Hao Yu, Luke Tierney and Ulrich Mansmann
Journal of statistical software, Vol.31(1), pp.1-27
08/01/2009
DOI: 10.18637/jss.v031.i01
url
https://doi.org/10.18637/jss.v031.i01View
Published (Version of record) Open Access

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.
 Computer Science Computer Science, Interdisciplinary Applications Mathematics Physical Sciences Science & Technology Statistics & Probability Technology

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