Programming with Big Data in R is a series of R packages and an environment for statistical computing with big data by using high-performance statistical computation. 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 systems. R system mainly focuses 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 and pbdMPI of pbdR.
The pbdR built on pbdMPI uses SPMD parallelism where every processor is considered as worker and owns parts of data. The 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 clustering analysis on high-dimensional large data. On the other hand, there is no restriction to use manager/workers parallelism in SPMD parallelism environment.
The Rmpi uses manager/workers parallelism where one main processor serves as the control of all other processors. The manager/workers parallelism introduced around early 2000 is particularly efficient for large tasks in small clusters, for example, bootstrap method and Monte Carlo simulation in applied statistics since i.i.d. assumption is commonly used in most statistical analysis. In particular, task pull parallelism has better performance for Rmpi in heterogeneous computing environments.
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.
Package design
Programming with pbdR requires usage of various packages developed by pbdR core team. Packages developed are the following.
General
I/O
Computation
Application
Profiling
Client/Server
pbdDEMO
pbdNCDF4
pbdDMAT
pmclust
pbdPROF
pbdZMQ
pbdMPI
pbdADIOS
pbdBASE
pbdML
pbdPAPI
remoter
pbdSLAP
hpcvis
pbdCS
kazaam
pbdRPC
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 parallel programming style
Among 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
Example 1
Hello World! Save the following code in a file called "demo.r"
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"
## Set a vector x on all processors with different values
N <- 5 x <- + N *.comm.rank
## All reduce x using summation operation
y <- allreduce comm.print y <- allreduce comm.print
## Finish
finalize
and use the command mpiexec -np 4 Rscript demo.r
to execute the code where 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 on a given matrix. Save the following code in a file called "demo.r"
Initialize process grid
library if comm.stop init.grid
Setup for the remainder
comm.set.seed M <- N <- 16 BL <- 2 # blocking --- passing single value BL assumes BLxBL blocking dA <- ddmatrix
LA SVD
svd1 <- La.svd comm.print
Finish
finalize
and use the command mpiexec -np 2 Rscript demo.r
to execute the code where Rscript is one of command line executable program.
