[{"@context":"http:\/\/schema.org\/","@type":"BlogPosting","@id":"https:\/\/wiki.edu.vn\/en\/wiki24\/programming-with-big-data-in-r\/#BlogPosting","mainEntityOfPage":"https:\/\/wiki.edu.vn\/en\/wiki24\/programming-with-big-data-in-r\/","headline":"Programming with Big Data in R","name":"Programming with Big Data in R","description":"From Wikipedia, the free encyclopedia Programming with Big Data in R (pbdR)[1] is a series of R packages and an","datePublished":"2016-08-03","dateModified":"2016-08-03","author":{"@type":"Person","@id":"https:\/\/wiki.edu.vn\/en\/wiki24\/author\/lordneo\/#Person","name":"lordneo","url":"https:\/\/wiki.edu.vn\/en\/wiki24\/author\/lordneo\/","image":{"@type":"ImageObject","@id":"https:\/\/secure.gravatar.com\/avatar\/c9645c498c9701c88b89b8537773dd7c?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/c9645c498c9701c88b89b8537773dd7c?s=96&d=mm&r=g","height":96,"width":96}},"publisher":{"@type":"Organization","name":"Enzyklop\u00e4die","logo":{"@type":"ImageObject","@id":"https:\/\/wiki.edu.vn\/wiki4\/wp-content\/uploads\/2023\/08\/download.jpg","url":"https:\/\/wiki.edu.vn\/wiki4\/wp-content\/uploads\/2023\/08\/download.jpg","width":600,"height":60}},"image":{"@type":"ImageObject","@id":"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/thumb\/e\/e3\/Pbd_overview.png\/220px-Pbd_overview.png","url":"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/thumb\/e\/e3\/Pbd_overview.png\/220px-Pbd_overview.png","height":"170","width":"220"},"url":"https:\/\/wiki.edu.vn\/en\/wiki24\/programming-with-big-data-in-r\/","wordCount":4358,"articleBody":"From Wikipedia, the free encyclopediaProgramming with Big Data in R (pbdR)[1] is a series of R packages and an environment for statistical computing with big data by using high-performance statistical computation.[2][3] The pbdR uses the same programming language as R with S3\/S4 classes and methods which is used among statisticians and data miners for developing statistical software. The significant difference between pbdR and R code is that pbdR mainly focuses on distributed memory systems, where data are distributed across several processors and analyzed in a batch mode, while communications between processors are based on MPI that is easily used in large high-performance computing (HPC) systems. R system mainly focuses[citation needed] on single multi-core machines for data analysis via an interactive mode such as GUI interface.Two main implementations in R using MPI are Rmpi[4] and pbdMPI of pbdR.The idea of 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 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).[5]Table of ContentsPackage design[edit]Examples[edit]Example 1[edit]Example 2[edit]Example 3[edit]Further reading[edit]References[edit]External links[edit]Package design[edit]Programming with pbdR requires usage of various packages developed by pbdR core team. Packages developed are the following.GeneralI\/OComputationApplicationProfilingClient\/ServerpbdDEMOpbdNCDF4pbdDMATpmclustpbdPROFpbdZMQpbdMPIpbdADIOSpbdBASEpbdMLpbdPAPIremoterpbdSLAPhpcvispbdCSkazaampbdRPC  The images describes how various pbdr packages are correlated.Among these packages, pbdMPI provides wrapper functions to MPI library, and it also produces a 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 OpenMPI or MPICH2 with a focus on Single Program\/Multiple Data (SPMD) parallel programming stylepbdSLAP — 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).pbdNCDF4 — interface to Parallel Unidata NetCDF4 format data filespbdBASE — low-level ScaLAPACK codes and wrapperspbdDMAT — distributed matrix classes and computational methods, with a focus on linear algebra and statisticspbdDEMO — set of package demonstrations and examples, and this unifying vignettepmclust — parallel model-based clustering using pbdRpbdPROF — profiling package for MPI codes and visualization of parsed statspbdZMQ — interface to \u00d8MQremoter — R client with remote R serverspbdCS — pbdR client with remote pbdR serverspbdRPC — remote procedure callkazaam — very tall and skinny distributed matricespbdML — machine learning toolboxAmong 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 that offers detailed explanations for the demos and provides some mathematical or statistical insight.Examples[edit]Example 1[edit]Hello World! Save the following code in a file called “demo.r”### Initial MPIlibrary(pbdMPI, quiet = TRUE)init()comm.cat(\"Hello World!n\")### Finishfinalize()and use the commandmpiexec -np 2 Rscript demo.rto execute the code where Rscript is one of command line executable program.Example 2[edit]The following example modified from pbdMPI illustrates the basic 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 mpiexec, mpirun, etc. Save the following code in a file called “demo.r”### Initial MPIlibrary(pbdMPI, quiet = TRUE)init().comm.size "},{"@context":"http:\/\/schema.org\/","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"item":{"@id":"https:\/\/wiki.edu.vn\/en\/wiki24\/#breadcrumbitem","name":"Enzyklop\u00e4die"}},{"@type":"ListItem","position":2,"item":{"@id":"https:\/\/wiki.edu.vn\/en\/wiki24\/programming-with-big-data-in-r\/#breadcrumbitem","name":"Programming with Big Data in R"}}]}]