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 littler 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 and Jay Emerson are gratefully acknowledged.

Direct support in R is starting with release 2.14.0 which includes a new package parallel incorporating (slightly revised) copies of packages multicore and snow (but excluding MPI, PVM and NWS clusters). Code written to use the higher-level API functions in those packages should work unchanged (apart from changing any references to their namespaces to a reference to parallel, and links explicitly to multicore or snow on help pages).

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

• Several packages provide the communications layer required for parallel computing. The first package in this area was rpvm by Li and Rossini which uses the PVM (Parallel Virtual Machine) standard and libraries. rpvm is no longer actively maintained.
• In recent years, the alternative MPI (Message Passing Interface) standard has become the de facto standard in parallel computing. It is supported in R via the Rmpi by Yu. Rmpi package is mature yet actively maintained and offers access to numerous functions from the MPI API, as well as a number of R-specific extensions. Rmpi can be used with the LAM/MPI, MPICH / MPICH2, Open MPI, and Deino MPI implementations. It should be noted that LAM/MPI is now in maintenance mode, and new development is focussed on Open MPI.
• An alternative is provided by the nws (NetWorkSpaces) packages from REvolution Computing. It is the successor to the earlier LindaSpaces approach to parallel computing, and is implemented on top of the Twisted networking toolkit for Python.
• The snow (Simple Network of Workstations) package by Tierney et al. can use PVM, MPI, NWS as well as direct networking sockets. It provides an abstraction layer by hiding the communications details. The snowFT package provides fault-tolerance extensions to snow.
• The snowfall package by Knaus provides a more recent alternative to snow. Functions can be used in sequential or parallel mode.
• The papply package by Currie provided a subset of the Rmpi functionality, but is no longer actively maintained either.
• The biopara package by Lazar and Schoenfeld offers socket-based parallel execution with some support for load-balancing and fault-tolerance.
• The taskPR package by Samatova et al. builds on top of LAM/MPI and offers parallel execution of tasks.
• The Simple Parallel R INTerface (SPRINT) package by Hill et al. ( link , paper ) provides a prototype framework that allows the addition of parallelised functions to R for easy exploitation of HPC systems. Currently only a parallised correlation calculation is provided.
• The foreach package allows general iteration over elements in a collection without the use of an explicit loop counter. Using foreach without side effects also facilitates executing the loop in parallel which is possible via the doMC (using multicore on single workstations), doSNOW (using snow, see above), doMPI (using Rmpi) packages and doRedis (using rredis) packages.
• The Rdsm package provides a threads-like parallel computing environment, both on multicore machine and across the network by providing facilities inspired from distributed shared memory programming.

Parallel computing: Implicit parallelism

• The pnmath package by Tierney ( link ) uses the Open MP parallel processing directives of recent compilers (such gcc 4.2 or later) for implicit parallelism by replacing a number of internal R functions with replacements that can make use of multiple cores --- without any explicit requests from the user. The alternate pnmath0 package offers the same functionality using Pthreads for environments in which the newer compilers are not available. Similar functionality is expected to become integrated into R 'eventually'.
• The romp package by Jamitzky was presented at useR! 2008 ( slides ) and offers another interface to Open MP using Fortran. The code is still pre-alpha and available from the Google Code project romp. An R-Forge project romp was initiated but there is no package, yet.
• The fork package by Warnes provides R-equivalents to low-level Unix system functions like fork, signal, wait, kill and exit in order to spawn sub-processes for parallel execution.
• The multicore package by Urbanek provides a way of running parallel computations in R on machines with multiple cores or CPUs by making use of operating system facilities.
• The R/parallel package by Vera, Jansen and Suppi offers a C++-based master-slave dispatch mechanism for parallel execution ( link )
• The SPRINT package by Hill adds another parallel framework to R ( link ).
• The mapReduce package by Brown provides a simple framework for parallel computations following the Google mapReduce approach. It provides a pure R implementation, a syntax following the mapReduce paper and a flexible and parallelizable back end.

Parallel computing: Grid computing

• The GridR package by Wegener et al. can be used in a grid computing environment via a web service, via ssh or via Condor or Globus.
• The multiR package by Grose was presented at useR! 2008 but has not been released. It may offer a snow-style framework on a grid computing platform.
• The biocep-distrib project by Chine offers a Java-based framework for local, Grid, or Cloud computing. It is under active development.
• The RHIPE package by Guha profides an interface between R and Hadoop for a Map/Reduce programming framework. ( link )
• The xgrid package provides functions to use Apple Xgrid clusters from within R.

Parallel computing: Random numbers

• Random-number generators for parallel computing are available via the rsprng package by Li, and the rlecuyer package by Sevcikova and Rossini.
• The doRNG package provides functions to perform reproducible parallel foreach loops, using independent random streams as generated by the package rstream, suitable for the different foreach backends.

Parallel computing: Resource managers and batch schedulers

• Job-scheduling toolkits permit management of parallel computing resources and tasks. The slurm (Simple Linux Utility for Resource Management) set of programs (written by a consortium led by Lawrence Livermore Labs) works well with MPI. ( link )
• The Condor toolkit ( link ) from the University of Wisconsin-Madison has been used with R as described in this R News article .
• The sfCluster package by Knaus can be used with snowfall. ( link ) but is currently limited to LAM/MPI.
• The Rsge package by Bode offers an interface to the Sun Grid Engine batch-queuing system.
• The batch package by Hoffmann can launch parallel computing requests onto a cluster and gather results.

Parallel computing: Applications

• The caret package by Kuhn can use various frameworks (MPI, NWS etc) to parallelized cross-validation and bootstrap characterizations of predictive models.
• The maanova package on Bioconductor by Wu can use snow and Rmpi for the analysis of micro-array experiments.
• The pvclust package by Suzuki and Shimodaira can use snow and Rmpi for hierarchical clustering via multiscale bootstraps; and the scaleboot package by Shimodaira can use pvclust, snow and Rmpi for computing approximately unbiased p-values via multiscale bootstraps.
• The tm package by Feinerer can use snow and Rmpi for parallelized text mining.
• The varSelRF package by Diaz-Uriarte can use snow and Rmpi for parallelized use of variable selection via random forests.
• The bcp package by Erdman and Emerson for the bayesian analysis of change points can use nws for parallelized operations.
• The multtest package by Pollard et al. on Bioconductor can use snow, Rmpi or rpvm for resampling-based testing of multiple hypothesis.
• The BARD package by Altman for better automated redistring, the GAMBoost package by Binder for glm and gam model fitting via boosting using b-splines, the Geneland package by Estoup, Guillot and Santos for structure detection from multilocus genetic data, the Matching package by Sekhon for multivariate and propensity score matching, the STAR package by Pouzat for spike train analysis, the bnlearn package by Scutari for bayesian network structure learning, the latentnet package by Krivitsky and Handcock for latent position and cluster models, the lga package by Harrington for linear grouping analysis, the peperr package by Porzelius and Binder for parallised estimation of prediction error, the orloca package by Fernandez-Palacin and Munoz-Marquez for operations research locational analysis, the rgenoud package by Mebane and Sekhon for genetic optimization using derivatives the affyPara package by Schmidberger, Vicedo and Mansmann for parallel normalization of Affymetrix microarrays, the puma package by Pearson et al. which propagates uncertainty into standard microarray analyses such as differential expression and the ccems package for combinatorically complex equilibrium model selection all can use snow for parallelized operations using either one of the MPI, PVM, NWS or socket protocols supported by snow.
• The bugsparallel package uses Rmpi for distributed computing of multiple MCMC chains using WinBUGS.
• The partDSA package uses nws for generating a piecewise constant estimation list of increasingly complex predictors based on an intensive and comprehensive search over the entire covariate space.
• The dclone package provides a global optimization approach and a variant of simulated annealing which exploits Bayesian MCMC tools to get MLE point estimates and standard errors using low level functions for implementing maximum likelihood estimating procedures for complex models using data cloning and Bayesian Markov chain Monte Carlo methods with support for JAGS, WinBUGS and OpenBUGS; parallel computing is supported via the snow package.
• The pmclust package utilizes unsupervised model-based clustering for high dimensional (ultra) large data. The package uses Rmpi to perform a parallel version of the EM algorithm for finite mixture Gaussian models.

Parallel computing: GPUs

• The gputools package by Buckner and Seligman provides several common data-mining algorithms which are implemented using a mixture of nVidia's CUDA langauge and cublas library. Given a computer with an nVidia GPU these functions may be substantially more efficient than native R routines. The rpud package provides an optimised distance metric for NVidia-based GPUs.
• The cudaBayesreg package by da Silva implements the rhierLinearModel from the bayesm package using nVidia's CUDA langauge and tools to provide high-performance statistical analysis of fMRI voxels.
• The rgpu package (see below for link) aims to speed up bioinformatics analysis by using the GPU.
• The magma package provides an interface to the hybrid GPU/CPU library Magma (see below for link).
• The gcbd package implements a benchmarking framework for BLAS and GPUs (using gputools).
• The OpenCL package provides an interface from R to OpenCL permitting hardware- and vendor neutral interfaces to GPU programming.

Large memory and out-of-memory data

• The biglm package by Lumley uses incremental computations to offers lm() and glm() functionality to data sets stored outside of R's main memory.
• The ff package by Adler et al. offers file-based access to data sets that are too large to be loaded into memory, along with a number of higher-level functions.
• The bigmemory package by Kane and Emerson permits storing large objects such as matrices in memory (as well as via files) and uses external pointer objects to refer to them. This permits transparent access from R without bumping against R's internal memory limits. Several R processes on the same computer can also shared big memory objects.
• A large number of database packages, and database-alike packages (such as sqldf by Grothendieck and data.table by Dowle) are also of potential interest but not reviewed here.
• The HadoopStreaming package provides a framework for writing map/reduce scripts for use in Hadoop Streaming; it also facilitates operating on data in a streaming fashion which does not require Hadoop.
• The speedglm package permits to fit (generalised) linear models to large data. For in-memory data sets, speedlm() or speedglm() can be used along with update.speedlm() which can update fitted models with new data. For out-of-memory data sets, shglm() is available; it works in the presence of factors and can check for singular matrices.
• The biglars package by Seligman et al can use the ff to support large-than-memory datasets for least-angle regression, lasso and stepwise regression.

Easier interfaces for Compiled code

• The inline package by Sklyar et al eases adding code in C, C++ or Fortran to R. It takes care of the compilation, linking and loading of embeded code segments that are stored as R strings.
• The Rcpp package by Eddelbuettel and Francois offers a number of C++ clases that makes transferring R objects to C++ functions (and back) easier, and the RInside package by the same authors allows easy embedding of R itself into C++ applications for faster and more direct data transfer.
• The rJava package by Urbanek provides a low-level interface to Java similar to the .Call() interface for C and C++.

Profiling tools

• The profr package by Wickham can visualize output from the Rprof interface for profiling.
• The proftools package by Tierney can also be used to analyse profiling output.