Programming with Big Data in R: Difference between revisions

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

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'''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 [[free software]] [[programming language]] and a softwarean environment for [[statistical computing]] with [[Big Data]] by utilizing high-performance statistical computation.<ref>{{cite web|author=Chen, W.-C. and Ostrouchov, G.|url=http://thirteen-01.stat.iastate.edu/snoweye/hpsc/|year=2011|title=HPSC -- High Performance Statistical Computing for Data Intensive Research}}</ref> The pbdR language is carried from [[R (programming language)|R]]<ref>{{cite book|author=R Core Team|title=R: A Language and Environment for Statistical Computing|year=2012|isbn=3-900051-07-0|url=http://www.r-project.org/}}</ref> used among [[statistician]]s and [[Data mining|data miners]] for developing [[statistical software]]. The pbdR codes mainly focuses on [[distributed memory]] system where data are distributed across several nodes, while communications between nodes are based on [[Message Passing Interface|MPI]] which is easily utilized in large [[High-performance computing|high-performance computing (HPC)]] systems.

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.

* 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]].

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]].