State-of-the-Art in Parallel Computing with R

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 useful for 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 four 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.

• Item Type: Paper (Technical Report)
• Published in: Journal of Statistical Software, No. 1, Vol. 31, August 2009:
• Keywords: R, high performance computing, parallel computing, computer cluster, multi-core systems, grid computing, benchmark.
• Subjects: Mathematics, Computer Science and Statistics > Statistics > Technical Reports
• Dewey Classification: 000 Computers, Information and General Reference > 004 Data processing computer science; 600 Natural sciences and mathematics > 510 Mathematics