Programming with Big Data in R: Difference between revisions

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{{notability|date=June 2013}}

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{{Infobox programming language

| name = pbdRbdrp

| logo = [[File:Pbdr.png|200px]]

| paradigm = [[SPMD]] and [[MPMD]]

| yearreleased = {{Start date and = Sep. age|2012|09}}

| designer = [http://thirteen-01.stat.iastate.edu/snoweye/mypage/ Wei-Chen Chen], [http://www.csm.ornl.gov/~ost George Ostrouchov], Pragneshkumar Patel, and [http://wrathematics.github.io/ Drew Schmidt]

| developer = pbdR Core Team

| latest_test_version = Through [[GitHub]] at [httphttps://github.com/RBigData/ RBigData]

| typing = [[dynamic typing|Dynamic]]

| influenced_by = [[R (programming language)|R]], [[C (programming language)|C]], [[Fortran (programming language)|Fortran]], and [[Message Passing Interface|MPI]], and [[ZeroMQ|ØMQ]]

| operating_system = [[Cross-platform]]

| license = [[General Public License]] and [[Mozilla Public License]]

| website = [http://{{URL|www.r-pbd.org r-pbd.org]}}

}}

'''Programming with Big Data in R''' (pbdR)<ref>{{cite web|author=Ostrouchov, G., Chen, W.-C., Schmidt, D., Patel, P.|title=Programming with Big Data in R|year=2012|url=http://r-pbd.org/}}</ref><ref>{{cite web|title=XSEDE|url=https://portal.xsede.org/knowledge-base/-/kb/document/bcrw}}</ref><ref name=pbdDEMO/> is a series of [[R (programming language)|R]] packages and an environment for [[statistical computing]] with [[Bigbig Datadata]] by utilizingusing high-performance statistical computation.<ref>{{cite web|authorauthor1=Chen, W.-C. and |author2=Ostrouchov, G.|name-list-style=amp|url=http://thirteen-01.stat.iastate.edu/snoweye/hpsc/|year=2011|title=HPSC -- High Performance Statistical Computing for Data Intensive Research|access-date=2013-06-25|archive-url=https://web.archive.org/web/20130719020318/http://thirteen-01.stat.iastate.edu/snoweye/hpsc/|archive-date=2013-07-19|url-status=dead}}</ref> The pbdR uses the same programming language as [[R (programming language)|R]]<ref name=R>{{cite bookweb|authorurl=R Core Teamhttps://learnshareit.com/tutorials-for-r/|title=R:Basic ATutorials Languagefor andR Environmentto forStart StatisticalAnalyzing ComputingData|year=2012|isbndate=3-900051-07-0|url=http://www.r-project.org/ November 2022 }}</ref> The pbdR uses the same programming language as R with [[S (programming language)|S3/S4]] classes and methods which is used among [[statistician]]s and [[Data mining|data miners]] for developing [[statistical software]]. The significant difference between pbdR and [[R (programmingcode language)|R]]<refis name=R/> codes isthat pbdR mainly focuses on [[distributed memory]] systemsystems, where data are distributed across several processors and analyzed in a [[Batch processing|batch mode]], while communications between processors are based on [[Message Passing Interface|MPI]] whichthat is easily utilizedused in large [[High-performance computing|high-performance computing (HPC)]] systems. [[R (programmingsystem language)|R]]mainly system<reffocuses{{Citation nameneeded|date=R/>July mainly focuses2013}} on single [[Multi-core processor| multi-core]] machines for data analysis via an interactive mode such as [[Graphical user interface|GUI interface]].<ref>Martinez, W. L. (2011), Graphical user interfaces. WIREs Comp Stat, 3: 119–133. doi: 10.1002/wics.150</ref>

Two main implementations in [[R (programming language)|R]] using [[Message Passing Interface|MPI]] are [http://cran.r-project.org/package=Rmpi Rmpi]<ref name=rmpi/> and [http://cran.r-project.org/package=pbdMPI pbdMPI] of pbdR.

* The pbdR built on [http://cran.r-project.org/package=pbdMPI pbdMPI] uses [[SPMD|SPMD parallelism]] where every processors are considered as workers and own parts of data. The [[SPMD|SPMD parallelism]]<ref name=spmd/><ref name=spmd_ostrouchov/> introduced in mid 1980 is particularly efficient in homogeneous computing environments for large data, for example, performing [[Singular value decomposition|singular value decomposition]]<ref>{{Cite book | last1=Golub | first1=Gene H. | author1-link=Gene H. Golub | last2=Van Loan | first2=Charles F. | author2-link=Charles F. Van Loan | title=Matrix Computations | publisher=Johns Hopkins | edition=3rd | isbn=978-0-8018-5414-9 | year=1996 }}

</ref> on a large matrix, or performing [[Mixture model|clustering analysis]] on high-dimensional large data. On the other hand, there is no restriction to use [[Master/slave (technology)|manager/workers parallelism]] in [[SPMD|SPMD parallelism]] environment.

* The [http://cran.r-project.org/package=Rmpi Rmpi]<ref name=rmpi/> uses [[Master/slave (technology)|manager/workers parallelism]] where one main processor (manager) servers as the control of all other processors (workers). The [[Master/slave (technology)|manager/workers parallelism]]<ref>[http://userpages.uni-koblenz.de/~laemmel/MapReduce/paper.pdf "Google's MapReduce Programming Model -- Revisited"] — paper by Ralf Lämmel; from [[Microsoft]]</ref> introduced around early 2000 is particularly efficient for large tasks in small [[Computer cluster|clusters]], for example, [[Bootstrapping (statistics)|bootstrap method]] and [[Monte Carlo method|Monte Carlo simulation]] in applied statistics since [[Independent and identically distributed random variables|i.i.d.]] assumption is commonly used in most [[Statistics|statistical analysis]]. In particular, [http://math.acadiau.ca/ACMMaC/Rmpi/structure.html| task pull] parallelism has better performance for Rmpi in heterogeneous computing environments.

The idea of [[SPMD|SPMD parallelism]] is to let every processors do the same works but on different parts of large data. For example, modern [[Graphics processing unit|GPU]] is a large collection of slower co-processors which can simply apply the same computation on different parts of relatively smaller data, but the [[SPMD|SPMD parallelism]] ends up an efficient way to obtain final solutions, i.e. time to solution is shorter.<ref>{{cite web | url = http://www.engadget.com/2006/09/29/stanford-university-tailors-folding-home-to-gpus/ | title = Stanford University tailors Folding@home to GPUs | author = Darren Murph | accessdate = 2007-10-04 }}</ref><ref>{{cite web | url = http://graphics.stanford.edu/~mhouston/ | title = Folding@Home - GPGPU | author = Mike Houston | accessdate = 2007-10-04 }}</ref> It is clearly that pbdR is not only suitable for small [[Computer cluster|clusters]], but also is stabler for analyzing [[Big data]] and is more scalable for [[Supercomputer|supercomputers]].<ref>{{cite journal|author=Schmidt, D., Ostrouchov, G., Chen, W.-C., and Patel, P.|title=Tight Coupling of R and Distributed Linear Algebra for High-Level Programming with Big Data|year=2012|pages=811-815|journal=High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion:|url=http://dl.acm.org/citation.cfm?id=2477156}}</ref> In short, pbdR

* does '''not''' like Rmpi, snow, snowfall, do-like, '''nor''' parallel packages in R,

* does '''not''' focus on interactive computing '''nor''' master/workers,

* but is able to use '''both''' SPMD and task parallelisms.

Two main implementations in [[R (programming language)|R]] using [[Message Passing Interface|MPI]] are Rmpi<ref name=rmpi>{{cite journal|author=Yu, H.|title=Rmpi: Parallel Statistical Computing in R|year=2002|url=https://cran.r-project.org/package=Rmpi|journal=R News}}</ref> and pbdMPI of pbdR.

* The pbdR built on pbdMPI uses [[SPMD|SPMD parallelism]] where every processor is considered as worker and owns parts of data. The [[SPMD|SPMD parallelism]] introduced in mid 1980 is particularly efficient in homogeneous computing environments for large data, for example, performing [[singular value decomposition]] on a large matrix, or performing [[Mixture model|clustering analysis]] on high-dimensional large data. On the other hand, there is no restriction to use [[Master/slave (technology)|manager/workers parallelism]] in [[SPMD|SPMD parallelism]] environment.

* The Rmpi<ref name=rmpi/> uses [[Master/slave (technology)|manager/workers parallelism]] where one main processor (manager) serves as the control of all other processors (workers). The [[Master/slave (technology)|manager/workers parallelism]] introduced around early 2000 is particularly efficient for large tasks in small [[Computer cluster|clusters]], for example, [[Bootstrapping (statistics)|bootstrap method]] and [[Monte Carlo method|Monte Carlo simulation]] in applied statistics since [[Independent and identically distributed random variables|i.i.d.]] assumption is commonly used in most [[Statistics|statistical analysis]]. In particular, task pull parallelism has better performance for Rmpi in heterogeneous computing environments.

The idea of [[SPMD|SPMD parallelism]] is to let every processor do the same amount of work, but on different parts of a large data set. For example, a modern [[Graphics processing unit|GPU]] is a large collection of slower co-processors that can simply apply the same computation on different parts of relatively smaller data, but the SPMD parallelism ends up with an efficient way to obtain final solutions (i.e. time to solution is shorter).<ref>{{cite web | url = http://graphics.stanford.edu/~mhouston/ | title = Folding@Home - GPGPU | author = Mike Houston | access-date = 2007-10-04 }}</ref>

== Package design ==

Line 35 ⟶ 29:

{| class="wikitable"

|-

! General !! I/O !! Computation !! Application !! Profiling !! Client/Server

|-

| pbdDEMO || pbdNCDF4 || pbdDMAT || pmclust || pbdPROF || pbdZMQ

| [http://cran.r-project.org/package=pbdDEMO pbdDEMO] || [http://cran.r-project.org/package=pbdNCDF4 pbdNCDF4] || [http://cran.r-project.org/package=pbdDMAT pbdDMAT] || [http://cran.r-project.org/package=pmclust pmclust]

|-

| pbdMPI || pbdADIOS || pbdBASE || pbdML || pbdPAPI || remoter

| [http://cran.r-project.org/package=pbdMPI pbdMPI] || || [http://cran.r-project.org/package=pbdBASE pbdBASE] ||

|-

| || || [http://cran.r-project.org/package=pbdSLAP pbdSLAP]|| || hpcvis || pbdCS

|-

| || || kazaam || || || pbdRPC

|}

[[File:Pbd overview.png|thumb|The images describes how various pbdr packages are correlated.]]

Among these packages, pbdMPI provides wrapper functions to [[MPI]] library, and it also produces a [[Library (computing)|shared library]] and a configuration file for [[MPI]] environments. All other packages rely on this configuration for installation and library loading that avoid difficulty of library linking and compiling. All other packages can directly utilize [[MPI]] functions easily.

Among these packages, pbdMPI provides wrapper functions to [[Message Passing Interface|MPI]] library, and it also produces a [[Library (computing)|shared library]] and a configuration file for MPI environments. All other packages rely on this configuration for installation and library loading that avoids difficulty of library linking and compiling. All other packages can directly use MPI functions easily.

* [http://cran.r-project.org/web/packages/pbdMPI/vignettes/pbdMPI-guide.pdf pbdMPI] --- an efficient interface to [[MPI]] either [[Open MPI|OpenMPI]]<ref> or [[MPICH2]] with a focus on Single Program/Multiple Data ([[SPMD]]) parallel programming style

* pbdSLAP --- bundles scalable dense linear algebra libraries in double precision for R, based on [[ScaLAPACK]] version 2.0.2 which includes several scalable linear algebra packages (namely [[BLACS]], [[PBLAS]], and [[ScaLAPACK]]).

{{cite web

* pbdNCDF4 --- interface to Parallel Unidata [[NetCDF]]4 format data files

| url=http://www.open-mpi.org/papers/sc-2008/jsquyres-cisco-booth-talk-1up.pdf

* pbdBASE --- low-level [[ScaLAPACK]] codes and wrappers

|author=Jeff Squyres

* pbdDMAT --- distributed matrix classes and computational methods, with a focus on linear algebra and statistics

| publisher=Open MPI Project

* pbdDEMO --- set of package demonstrations and examples, and this unifying vignette

| title=Open MPI: 10^15 Flops Can't Be Wrong

* pmclust --- parallel [[model-based clustering]] using pbdR

| accessdate=2011-09-27}}</ref> or [[MPICH2]]<ref>[http://www.mcs.anl.gov/research/projects/mpich2/downloads/license.txt MPICH License]</ref> with a focus on Single Program/Multiple Data ([[SPMD]]) parallel programming style<ref name=spmd>{{cite journal|author=Darema, F.|title=The SPMD Model: Past, Present and Future|url=http://dx.doi.org/10.1007/3-540-45417-9_1|year=2001}}</ref><ref>{{cite journal|author=Ortega, J.M., Voight, G.G., and Romine, C.H.|year=1989|title=Bibliography on Parallel and Vector Numerical Algorithms|url=http://liinwww.ira.uka.de/bibliography/Parallel/ovr.html}}</ref><ref name=spmd_ostrouchov>{{cite journal|author=Ostrouchov, G.|year=1987|title=Parallel Computing on a Hypercube: An Overview of the Architecture and Some Applications|journal=Proc. 19th Symp. on the Interface of Computer Science and Statistics|page=27-32}}</ref>

* pbdPROF --- profiling package for MPI codes and visualization of parsed stats

* [http://cran.r-project.org/web/packages/pbdSLAP/vignettes/pbdSLAP-guide.pdf pbdSLAP] --- bundles scalable dense linear algebra libraries in double precision for R, based on [[ScaLAPACK]] version 2.0.2<ref>{{cite book|title=ScaLAPACK Users' Guide|author=Blackford, L.S., et.al.|year=1997|url=http://netlib.org/scalapack/slug/scalapack_slug.html/}}</ref> which includes several scalable linear algebra packages (namely [[BLACS]], [[PBLAS]], and [[ScaLAPACK]]).<ref>{{cite web|title=PBLAS|url=http://www.netlib.org/utk/papers/scalapack/node9.html|work=Netlib|first=Antoine |last=Petitet

* pbdZMQ --- interface to [[ZeroMQ|ØMQ]]

|year=1995|accessdate= 13 July 2012}}</ref><ref name=pbblas>{{cite journal|title=PB-BLAS: a set of Parallel Block Basic Linear Algebra Subprograms|journal=Scalable High-Performance Computing Conference|year=1994|month=May|pages=534–541|url=http://www.netlib.org/utk/people/JackDongarra/journals/079_1996_pb-blas-a-set-of-parallel-block-basic-linear-algebra-subroutines.pdf|doi=10.1109/SHPCC.1994.296688|isbn=0-8186-5680-8|last1=Jaeyoung Choi|last2=Dongarra|first2=J.J.|last3=Walker|first3=D.W.}}</ref>

* remoter --- R client with remote R servers

* [http://cran.r-project.org/web/packages/pbdNCDF4/vignettes/pbdNCDF4-guide.pdf pbdNCDF4] --- Interface to Parallel Unidata [[NetCDF|NetCDF4]] format data files<ref>{{cite web|title=Network Common Data Form|author=NetCDF Group|url=http://www.unidata.ucar.edu/software/netcdf/|year=2008}}</ref>

* pbdCS --- pbdR client with remote pbdR servers

* [http://cran.r-project.org/web/packages/pbdBASE/vignettes/pbdBASE-guide.pdf pbdBASE] --- low-level [[ScaLAPACK]] codes and wrappers

* pbdRPC --- remote procedure call

* [http://cran.r-project.org/web/packages/pbdDMAT/vignettes/pbdDMAT-guide.pdf pbdDMAT] --- distributed matrix classes and computational methods, with a focus on linear algebra and statistics<ref>{{cite journal|author=J. Dongarra and D. Walker|title=The Design of Linear Algebra Libraries for High Performance Computers|url=http://acts.nersc.gov/scalapack/hands-on/datadist.html}}</ref><ref>{{cite journal|author=J. Demmel, M. Heath, and H. van der Vorst|title=Parallel Numerical Linear Algebra|url=http://acts.nersc.gov/scalapack/hands-on/datadist.html}}</ref><ref>{{cite web|title=2d block-cyclic data layout|url=http://acts.nersc.gov/scalapack/hands-on/datadist.html}}</ref>

* kazaam --- very tall and skinny distributed matrices

* [http://cran.r-project.org/web/packages/pbdDEMO/vignettes/pbdDEMO-guide.pdf pbdDEMO] --- set of package demonstrations and examples, and this unifying vignette<ref name=pbdDEMO>{{cite journal|author=Schmidt, D., Chen, W.-C., Patel, P., Ostrouchov, G.|year=2013|title=Speaking Serial R with a Parallel Accent|url=http://github.com/wrathematics/pbdDEMO/blob/master/inst/doc/pbdDEMO-guide.pdf?raw=true}}</ref>

* pbdML --- machine learning toolbox

* [http://cran.r-project.org/web/packages/pmclust/vignettes/pmclust-guide.pdf pmclust] -- parallel [[Mixture model|model-based clustering]] using pbdR

AmountAmong those packages, the pbdDEMO package is a collection of 20+ package demos which offer example uses of the various pbdR packages, and contains a vignette whichthat offers detailed explanations for the demos and provides some mathematical or statistical insight.

== Examples ==

=== Example 1 ===

Hello World! Save the following code in a file called ``"demo.r``"

<sourcesyntaxhighlight lang="rsplusr">

### Initial MPI

library(pbdMPI, quiet = TRUE)

Line 75 ⟶ 72:

### Finish

finalize()

</syntaxhighlight>

</source>

and use the command

<sourcesyntaxhighlight lang="bash">

mpiexec -np 2 Rscript demo.r

</syntaxhighlight>

</source>

to execute the code where [[R (programming language)|Rscript]] is one of command line executable program.

=== Example 2 ===

The following example modified from pbdMPI illustrates the basic [[programming language syntax|syntax of the language]] of pbdR.

Since pbdR is designed in [[SPMD]], all the R scripts are stored in files and executed from the command line via [[MPI|mpiexec]], [[MPI|mpirun]], etc. Save the following code in a file called ``"demo.r``"

<sourcesyntaxhighlight lang="rsplusr">

### Initial MPI

library(pbdMPI, quiet = TRUE)

Line 104 ⟶ 101:

### Finish

finalize()

</syntaxhighlight>

</source>

and use the command

<sourcesyntaxhighlight lang="bash">

mpiexec -np 4 Rscript demo.r

</syntaxhighlight>

</source>

to execute the code where [[R (programming language)|Rscript]] is one of command line executable program.

=== Example 3 ===

The following example modified from pbdDEMO illustrates the basic ddmatrix computation of pbdR which performs [[Singular value decomposition|singular value decomposition]] on a given matrix.

Save the following code in a file called ``"demo.r``"

<sourcesyntaxhighlight lang="rsplusr">

# Initialize process grid

library(pbdDMAT, quiet=T)

Line 133 ⟶ 130:

# Finish

finalize()

</syntaxhighlight>

</source>

and use the command

<sourcesyntaxhighlight lang="bash">

mpiexec -np 2 Rscript demo.r

</syntaxhighlight>

</source>

to execute the code where [[R (programming language)|Rscript]] is one of command line executable program.

== Further reading ==

* [http://www.umbc.edu/hpcf/publications/index.html#pbdRtara2013 Introduction to distributed computing with pbdR at the UMBC High Performance Computing Facility (Technical Report, 2013)].<ref>{{cite journaltech report|author=Raim, A.M.|year=2013|title= Introduction to distributed computing with pbdR at the UMBC High Performance Computing Facility|journalinstitution=UMBC TechnicalHigh ReportPerformance Computing Facility, University of Maryland, Baltimore County|number=HPCF-2013-2|url=http://wwwuserpages.umbc.edu/hpcf~gobbert/publicationspapers/indexpbdRtara2013.html#pdf|accessdate=2013-06-26|archiveurl=https://web.archive.org/web/20140204051402/http://userpages.umbc.edu/~gobbert/papers/pbdRtara2013.pdf|archivedate=2014-02-04|url-status=dead}}</ref>

* {{cite tech report|author=Bachmann, M.G., Dyas, A.D., Kilmer, S.C. and Sass, J.|year=2013|title=Block Cyclic Distribution of Data in pbdR and its Effects on Computational Efficiency|institution=UMBC High Performance Computing Facility, University of Maryland, Baltimore County|number=HPCF-2013-11|url=http://userpages.umbc.edu/~gobbert/papers/REU2013Team1.pdf|accessdate=2014-02-01|archiveurl=https://web.archive.org/web/20140204051351/http://userpages.umbc.edu/~gobbert/papers/REU2013Team1.pdf|archivedate=2014-02-04|url-status=dead}}

* [http://cran.r-project.org/ CRAN] Task View: [http://cran.r-project.org/web/views/HighPerformanceComputing.html High-Performance and Parallel Computing with R].<ref>{{cite web|title=High-Performance and Parallel Computing with R|author=Dirk Eddelbuettel|url=http://cran.r-project.org/web/views/HighPerformanceComputing.html}}</ref>

* {{cite tech report|author=Bailey, W.J., Chambless, C.A., Cho, B.M. and Smith, J.D.|year=2013|title=Identifying Nonlinear Correlations in High Dimensional Data with Application to Protein Molecular Dynamics Simulations|institution=UMBC High Performance Computing Facility, University of Maryland, Baltimore County|number=HPCF-2013-12|url=http://userpages.umbc.edu/~gobbert/papers/REU2013Team2.pdf|accessdate=2014-02-01|archiveurl=https://web.archive.org/web/20140204055902/http://userpages.umbc.edu/~gobbert/papers/REU2013Team2.pdf|archivedate=2014-02-04|url-status=dead}}

* [http://www.r-bloggers.com/r-at-12000-cores/ R at 12,000 Cores].<ref>{{cite news|title=R at 12,000 Cores|url=http://www.r-bloggers.com/r-at-12000-cores/}}</ref> This article was read 22,584 times in 2012 since it posted on October 16, 2012 and ranked number 3 according to [http://www.r-bloggers.com/100-most-read-r-posts-for-2012-stats-from-r-bloggers-big-data-visualization-data-manipulation-and-other-languages/|Top 100 R posts of 2012]<ref>{{cite news|url=http://www.r-bloggers.com/100-most-read-r-posts-for-2012-stats-from-r-bloggers-big-data-visualization-data-manipulation-and-other-languages/|title=100 most read R posts in 2012 (stats from R-bloggers) – big data, visualization, data manipulation, and other languages}}</ref>

* {{cite web|title=High-Performance and Parallel Computing with R|author=Dirk Eddelbuettel|date=13 November 2022 |url=https://cran.r-project.org/web/views/HighPerformanceComputing.html|author-link=Dirk Eddelbuettel}}

* [http://rwiki.sciviews.org/doku.php?id=developers:projects:gsoc2013:mpiprofiler|MPI Profiler for pbdR] mentored by the [http://rwiki.sciviews.org/doku.php| Organization of R Project for Statistical Computing] for Google summer of code 2013.<ref>{{cite web|url=http://rwiki.sciviews.org/doku.php?id=developers:projects:gsoc2013:mpiprofiler|title=Profiling Tools for Parallel Computing with R|author=GSOC-R 2013}}</ref>

* {{cite news|title=R at 12,000 Cores|url=http://www.r-bloggers.com/r-at-12000-cores/}}<br />This article was read 22,584 times in 2012 since it posted on October 16, 2012, and ranked number 3<ref>{{cite news|url=http://www.r-bloggers.com/100-most-read-r-posts-for-2012-stats-from-r-bloggers-big-data-visualization-data-manipulation-and-other-languages/|title=100 most read R posts in 2012 (stats from R-bloggers) – big data, visualization, data manipulation, and other languages}}</ref>

* {{cite web|url=http://rwiki.sciviews.org/doku.php?id=developers:projects:gsoc2013:mpiprofiler|archive-url=https://archive.today/20130629095333/http://rwiki.sciviews.org/doku.php?id=developers:projects:gsoc2013:mpiprofiler|url-status=dead|archive-date=2013-06-29|title=Profiling Tools for Parallel Computing with R|author=Google Summer of Code - R 2013}}

== External links ==

* {{cite web|url=http://rpubs.com/wush978/pbdMPI-linux-pilot|title=在雲端運算環境使用R和MPI|author=Wush Wu (2014)}}

* {{Official website|r-pbd.org}} of the pbdR project

* {{cite web|url=https://www.youtube.com/watch?v=m1vtPESsFqM|title=快速在AWS建立R和pbdMPI的使用環境|author=Wush Wu (2013)|website=[[YouTube]] }}

* [http://thirteen-01.stat.iastate.edu/snoweye/pbdr/ Technical website] of the pbdR packages

* [http://code.r-pbd.org Source Code] of developing version of the pbdR packages

* [http://group.r-pbd.org Discussion Group] for any of pbdR related topics

== Milestones ==

2013

* Version 1.0-2:&nbsp; Add pmclust.

* Version 1.0-1:&nbsp; Add pbdNCDF4.

* Version 1.0-0:&nbsp; Add pbdDEMO.

2012

* Version 0.1-2:&nbsp; Add pbdBASE and pbdDMAT.

* Version 0.1-1:&nbsp; Add pbdSLAP.

* Version 0.1-0:&nbsp; Migrate from [http://cran.r-project.org/package=Rmpi Rmpi]<ref name=rmpi>{{cite journal|author=Yu, H.|title=Rmpi: Parallel Statistical Computing in R|year=2002|url=http://cran.r-project.org/package=Rmpi|journal=R News}}</ref> to pbdMPI.

== References ==

{{Reflist|30em}}

== External links ==

* {{Official website|www.r-pbd.org}}

{{DEFAULTSORT:PbdR}}

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