CRAN Task View: High-Performance and Parallel Computing with R

Maintainer:Dirk Eddelbuettel
Contact:Dirk.Eddelbuettel at

This CRAN task view contains a list of packages, grouped by topic, that are useful for high-performance computing (HPC) with R. In this context, we are defining 'high-performance computing' rather loosely as just about anything related to pushing R a little further: using compiled code, parallel computing (in both explicit and implicit modes), working with large objects as well as profiling.

Unless otherwise mentioned, all packages presented with hyperlinks are available from CRAN, the Comprehensive R Archive Network.

Several of the areas discussed in this Task View are undergoing rapid change. Please send suggestions for additions and extensions for this task view to the task view maintainer .

Suggestions and corrections by Achim Zeileis, Markus Schmidberger, Martin Morgan, Max Kuhn, Tomas Radivoyevitch, Jochen Knaus, Tobias Verbeke, Hao Yu, David Rosenberg, Marco Enea, Ivo Welch, Jay Emerson, Wei-Chen Chen, Bill Cleveland, Ross Boylan, Ramon Diaz-Uriarte, Mark Zeligman, Kevin Ushey, Graham Jeffries, and Will Landau (as well as others I may have forgotten to add here) are gratefully acknowledged.

Contributions are always welcome, and encouraged. Since the start of this CRAN task view in October 2008, most contributions have arrived as email suggestions. The source file for this particular task view file now also reside in a GitHub repository (see below) so that pull requests are also possible.

The ctv package supports these Task Views. Its functions install.views and update.views allow, respectively, installation or update of packages from a given Task View; the option coreOnly can restrict operations to packages labeled as core below.

Direct support in R started with release 2.14.0 which includes a new package parallel incorporating (slightly revised) copies of packages multicore and snow. Some types of clusters are not handled directly by the base package 'parallel'. However, and as explained in the package vignette, the parts of parallel which provide snow -like functions will accept snow clusters including MPI clusters.
The parallel package also contains support for multiple RNG streams following L'Ecuyer et al (2002), with support for both mclapply and snow clusters.
The version released for R 2.14.0 contains base functionality: higher-level convenience functions are planned for later R releases.

Parallel computing: Explicit parallelism

Parallel computing: Implicit parallelism

Parallel computing: Grid computing

Parallel computing: Hadoop

Parallel computing: Random numbers

Parallel computing: Resource managers and batch schedulers

Parallel computing: Applications

Parallel computing: GPUs

Large memory and out-of-memory data

Easier interfaces for Compiled code

Profiling tools

CRAN packages:

Related links: