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, and Kevin Ushey
(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.
package supports these Task Views. Its functions
respectively, installation or update of packages from a given Task View;
can restrict operations to packages labeled as
Direct support in R started with release 2.14.0
which includes a new package
(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
-like functions will
clusters including MPI clusters.
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 rpvm is no longer actively
maintained, but available from its CRAN archive directory.
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
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.
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.
package provides S4 classes to directly interface
MPI in order to support the Single Program/Multiple Data (SPMD) parallel
programming style which is particularly useful for batch parallel execution.
builds on this and uses scalable linear algebra
packages (namely BLACS, PBLAS, and ScaLAPACK) in double precision based
on ScaLAPACK version 2.0.2.
builds on these and provides the core classes
and methods for distributed data types upon which the
builds to provide distributed dense matrices for "Programming
with Big Data". The
multiple processes to write to the same file (without manual
synchronization) and supports terabyte-sized files.
package provides examples for these
packages, and a detailed vignette.
package profiles MPI communication SPMD code
via MPI profiling libraries, such as fpmpi, mpiP, or TAU.
An alternative is provided by the
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.
(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
fault-tolerance extensions to
package by Knaus provides a more recent
snow. Functions can be used in sequential or
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
(using parallel/multicore on single
package allows for synchroneous (sequential)
and asynchronous (parallel) evaluations via abstraction of futures,
either via function calls or implicitly via promises. Global variables
are automatically identified. Iteration over elements in a collection
package employs OpenMP pragmas to exploit
predictor-level parallelism in the Random Forest algorithm which
promotes efficient use of multicore hardware in restaging data and in
determining splitting criteria, both of which are performance
bottlenecks in the algorithm.
package connects to the h2o open source machine
learning environment which has scalable implementations of random
forests, GBM, GLM (with elastic net regularization), and deep learning.
package can use both OpenMP as well
as MPI for random forest extensions suitable for survival analysis,
competing risks analysis, classification as well as regression
Parallel computing: Implicit parallelism
The pnmath package by Tierney
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
and offers another interface to Open MP using Fortran. The code is still
pre-alpha and available from the Google Code project
An R-Forge project
was initiated but there is no package, yet.
The R/parallel package by Vera, Jansen and Suppi offers a C++-based master-slave dispatch
mechanism for parallel execution (
package provides a threads-like parallel
computing environment, both on multicore machine and across the network
by providing facilities inspired from distributed shared memory
detects the number of available
BLAS cores, and permits explicit selection of the number of
style dispatch via MPI.
Parallel computing: Grid computing
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.
project by Chine offers a Java-based framework for local, Grid,
or Cloud computing. It is under active development.
Parallel computing: Hadoop
The RHIPE package, started by Saptarshi Guha and now developed by a core team via
provides an interface between R and Hadoop for analysis of large complex data wholly from
within R using the Divide and Recombine approach to big data.
The rmr package by Revolution Analytics also provides an interface between R and Hadoop
for a Map/Reduce programming framework. (
A related package, segue package by Long, permits easy execution of embarassingly parallel task on Elastic Map Reduce (EMR) at Amazon.
package provides an interface to
Google's language-neutral, platform-neutral, extensible
mechanism for serializing structured data. This package can
be used in R code to read data streams from other systems in a
distributed MapReduce setting where data is serialized and
passed back and forth between tasks.
package provides a number of
routines useful for the construction, aggregation,
manipulation, and plotting of large numbers of Histograms such
as those created by Mappers in a MapReduce application.
package performs in-database computations
utilizing the parallel / distributed Teradata Aster analytical platform
Parallel computing: Random numbers
Random-number generators for parallel computing are available via
package by Sevcikova and Rossini.
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 works well with
MPI and slurm jobs can be submitted from R using the
The Condor toolkit (
the University of Wisconsin-Madison has been used with R as described
The sfCluster package by Knaus can be used with
) but is
currently limited to LAM/MPI.
package by Hoffmann can launch parallel computing
requests onto a cluster and gather results.
package provides Map, Reduce and
Filter variants to manage R jobs and their results on batch
computing systems like PBS/Torque, LSF and Sun Grid
Engine. Multicore and SSH systems are also supported. The
package extends it with an
abstraction layer for running statistical experiments. Package
is a successor / extension to both.
package offers a scatter-gather approach to submit jobs
lists (including dependencies) to the computing cluster via simple data.frames
as inputs. It supports LSF, SGE, Torque and SLURM.
Parallel computing: Applications
package by Kuhn can use various frameworks
(MPI, NWS etc) to parallelized cross-validation and bootstrap characterizations of predictive models.
package on Bioconductor by Wu can use
for the analysis of micro-array experiments.
package by Suzuki and Shimodaira can use
for hierarchical clustering via multiscale
package by Feinerer can use
for parallelized text mining.
package by Diaz-Uriarte can use
for parallelized use of variable selection via
package by Erdman and Emerson for the Bayesian
analysis of change points can use
for parallelized operations.
package by Pollard et al. on Bioconductor can
or rpvm for
resampling-based testing of multiple hypothesis.
package by Binder for
model fitting via boosting using b-splines,
package by Estoup, Guillot and Santos for
structure detection from multilocus genetic data,
package by Sekhon for multivariate and propensity
package by Pouzat for spike train analysis,
package by Scutari for bayesian network
package by Krivitsky and Handcock for latent
position and cluster models,
package by Harrington for linear grouping analysis,
package by Porzelius and Binder for parallised
estimation of prediction error,
package by Fernandez-Palacin and Munoz-Marquez
for operations research locational analysis,
package by Mebane and Sekhon for genetic
optimization using derivatives
package by Schmidberger, Vicedo and
Mansmann for parallel normalization of Affymetrix microarrays,
package by Pearson et al. which propagates
uncertainty into standard microarray analyses such as differential
all can use
for parallelized operations using either
one of the MPI, PVM, NWS or socket protocols supported by
computing of multiple MCMC chains using WinBUGS.
for generating a
piecewise constant estimation list of increasingly complex
predictors based on an intensive and comprehensive search over the
entire covariate space.
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
package utilizes unsupervised model-based
clustering for high dimensional (ultra) large data. The package uses
to perform a parallel version of the EM algorithm for
finite mixture Gaussian models.
package provides helper functions
for (reproducible) simulations.
Nowadays, many packages can use the facilities offered by
package. One example
pls, another is
which can run ICA
analysis in parallel on SGE or multicore platforms.
(an acronym for "Simple Parallel R INTerface")
package provides a parallel computing framework for R making High
Performance Computing (HPC) accessible to users who are not familiar
with parallel programming and the use of HPC architectures. It
contains a library of parallelised R functions for correlation,
partitioning around medoids, apply, permutation testing,
bootstrapping, random forest, rank product and hamming distance.
package offers a progress bar for vectorized R
functions in the `*apply` family, and supports several backends.
Parallel computing: GPUs
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
package by da Silva implements
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.
package implements a benchmarking framework for
BLAS and GPUs (using
package provides an interface from R to
OpenCL permitting hardware- and vendor neutral interfaces to
package provide High-Performance Linear
Algebra for R using multi-core and/or GPU support using the
PLASMA / MAGMA libraries from UTK, CUDA, and accelerated BLAS.
package computes permutation resampling
inference in the context of RNA microarray studies on the GPU,
it uses CUDA (>= 4.5)
package enables the evaluation of matrix and vector
operations using GPU coprocessors such that intermediate computations may be
kept on the coprocessor and reused, with potentially significant performance
enhancements by minimizing data movement.
package offers GPU-enabled functions: New gpu*
and vcl* classes are provided to wrap typical R objects (e.g. vector,
matrix) mirroring typical R syntax without the need to know OpenCL.
Large memory and out-of-memory data
package by Lumley uses incremental computations to
data sets stored outside of R's main memory.
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
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 share big memory objects.
A large number of database packages, and database-alike packages
by Grothendieck and
by Dowle) are also of potential interest but not reviewed here.
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
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
package by Seligman et al can use the
to support large-than-memory datasets for least-angle regression,
lasso and stepwise regression.
package allows R to access the MonetDB
column-oriented, open source database system as a
package by de Jonge et al adds basic
statistical functionality to the
package provides methods for fast access to
large ASCII files in csv or fixed-width format.
Easier interfaces for Compiled code
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.
package by Eddelbuettel and Francois offers a
number of C++ clases that makes transferring R objects to C++
functions (and back) easier, and the
by the same authors allows easy embedding of R itself into C++
applications for faster and more direct data transfer.
package by Allaire et al. bundles the
Intel Threading Building Blocks
libraries. Together with
Rcpp, RcppParallel makes it
easy to write safe, performant, concurrently-executing C++ code,
and use that code within R and R packages.
package by Urbanek provides a low-level
interface to Java similar to the
interface for C
package by Wickham can visualize output from
interface for profiling.
package by Tierney, and the
package by Visser, can also be used to analyse
package visualizes the results of
profiling R programs.