Programming with Big Data in R: Difference between revisions

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{{Infobox programming language

| name = pbdRbdrp

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

| paradigm = [[SPMD|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]], and[[C (programming language)|C]], [[Fortran]], [[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> is a [[freeseries software]]of [[R (programming language)|R]] packages and a softwarean 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 language is carried from [[R (programming language)|R]]<ref>{{cite bookweb|authorurl=R Core Teamhttps://learnshareit.com/tutorials-for-r/|title=R:Basic ATutorials Languagefor andR Environmentto forStart StatisticalAnalyzing Computing|year=2012Data|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 codesand R code is that pbdR mainly focuses on [[distributed memory]] systemsystems, where data are distributed across several nodesprocessors and analyzed in a [[Batch processing|batch mode]], while communications between nodesprocessors are based on [[Message Passing Interface|MPI]] whichthat is easily utilizedused in large [[High-performance computing|high-performance computing (HPC)]] systems. R system mainly focuses{{Citation needed|date=July 2013}} on single [[Multi-core processor|multi-core]] machines for data analysis via an interactive mode such as [[Graphical user interface|GUI interface]].

Two main implementations in [[R (programming language)|R]] using [[Message Passing Interface|MPI]] are [[Rmpi]]<ref name=rmpi/> and [[pbdMPI]] of pbdR.

* The [[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). This parallelism 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]].

* The pbdR built on [[pbdMPI]] uses [[SPMD|SPMD Parallelism]] where every processors are considered as workers and own parts of data. This parallelism is particularly for large data, for example, performing [[Singular value decomposition|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.

It is clearly that pbdR is suitable for small [[Computer cluster|clusters]], but is stabler for analyzing larger data and is more scalable for [[Supercomputer|supercomputers]].

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.

== Programming Features ==

* 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.

Programming with pbdR requires usage of various packages developed by pbdR core team.

* 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 ==

Packages developed are the following.

Programming with pbdR requires usage of various packages developed by pbdR core team. Packages developed are the following.

{| class="wikitable"

|-

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

|-

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

|-

| pbdMPI || pbdADIOS || pbdBASE || pbdML || pbdBASEpbdPAPI || remoter

|-

| || || 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 [[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.

* 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

|author=Jeff Squyres

| publisher=Open MPI Project

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

| 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>{{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>{{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>

* 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

|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>

* 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>

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

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

* 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>

* pbdDEMO --- set of package demonstrations and examples, and this unifying vignette<ref>{{cite journal|author=Schmidt, D., Chen, W.-C., Patel, P., Ostrouchov, G.|year=2013|title=Speaking Serial R with a Parallel Accent|url=http://cran.r-project.org/package=pbdDEMO}}</ref>

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

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

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

* remoter --- R client with remote R servers

* pbdCS --- pbdR client with remote pbdR servers

* pbdRPC --- remote procedure call

* kazaam --- very tall and skinny distributed matrices

* pbdML --- machine learning toolbox

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"

<syntaxhighlight lang="r">

### Initial MPI

library(pbdMPI, quiet = TRUE)

init()

comm.cat("Hello World!\n")

### Finish

finalize()

</syntaxhighlight>

and use the command

<syntaxhighlight lang="bash">

mpiexec -np 2 Rscript demo.r

</syntaxhighlight>

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 75 ⟶ 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 23 ===

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 104 ⟶ 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 ==

* {{cite tech report|author=Raim, A.M.|year=2013|title=Introduction to distributed computing with pbdR at the UMBC High Performance Computing Facility|institution=UMBC High Performance Computing Facility, University of Maryland, Baltimore County|number=HPCF-2013-2|url=http://userpages.umbc.edu/~gobbert/papers/pbdRtara2013.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}}

* [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>

* [http://userpages.umbc.edu/~gobbert/papers/pbdRtara2013.pdf{{cite UMBCtech HPCFreport|author=Bachmann, Technique Report byM.G., RaimDyas, A.MD., (2013)]Kilmer, S.C.<ref>{{cite journal|author=Raimand Sass, A.MJ.|year=2013|title=Block IntroductionCyclic toDistribution distributedof computingData within pbdR atand theits Effects on Computational Efficiency|institution=UMBC High Performance Computing Facility|journal=, TechnicalUniversity Reportof Maryland, Baltimore County|number=HPCF-2013-211|url=http://userpages.umbc.edu/~gobbert/papers/pbdRtara2013REU2013Team1.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}}</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>

* {{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}}

* {{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>

== External links ==

* {{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}}

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

* {{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)}}

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

* {{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://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 [[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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