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Using the package Rmpi requires a sound scripting in order to distribute the work and handle the communication. In addition, a script is necessary the make the workers available to R at the beginning. This part is usually shipped with an .Rprofile, located either in the working or in the HOME directory.

There are multiple ways to ensure that:

* copy the .Rprofile into the working directory (e.g. cp ~/R/x86_64-unknown-linux-gnu-library/2.15/Rmpi/Rprofile ./.Rprofile)

* .Rprofile located in HOME

* set the environment variable R_PROFILE that points to any file holding the required contents, this might be done within the jobfile, “.bashrc” or in a module file

Using the package doMPI enables parallization with foreach loops in R. See the package documentation for details.

In order to starts the jobs properly, there must not be a .Rprofile available within the current environment.

An example for doMPI and a corresponding job script.

start_par_R.sh

	#!/bin/bash
	# show commands being executed, per debug
	set -x
 
	# Set job parameters
	######################################################################
	## check what MPI implementation is loaded before setting the parameter
	## module list ??
	#BSUB -a openmpi
 
	# Set number of CPUs
	#BSUB -n 16
 
	# name of the output file
	#BSUB -o 16_cpus.out
 
 
	# Start R MPI job
	mpirun.lsf ./dompi_example.R

And the R-script that is called.

<file bash dompi_example.R>

################################################################################################
################################################################################################

################################################################################################

rm(list=ls())

################################################################################################
library(reshape)	# general stuff

library(cluster)	# used for the cluster algorithms
library(fpc)		# used for the cluster validation

library(doMPI)		# framework for the Rmpi package
library(foreach)	# parallel backend 

################################################################################################

# create some random data in a matrix, make them reproducible 

set.seed(1)
data=matrix(c(runif(300, min=100, max=1000), rnorm(300, mean=-100, sd=2)), ncol=2)

# create distance matrix the cluster algorithm requires
dist_mat=dist(data, method="euclidean")

#compute the cluster Dendrogramm for the data
cluster_fit=hclust(dist_mat, method="ward")

#	!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!	#
#	!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!	#
#	!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!	#
#	!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!	#

# 	That bit runs distributed over multiple nodes specified in the
# 	jobscript compute cluster validation indices for each cluster 
#	class, we only check classes 2 to nrow(data)/2

cl = startMPIcluster()	# we do not give a number here, which means we take as much as we
			# get- managed by the startup script

registerDoMPI(cl) 	# register cl for the foreach framework

cluster_groups=foreach(i=c(2:(nrow(data)/2)), .combine=cbind, 
			  .packages=c("cluster","fpc"))%dopar%
{
	cluster.stats(dist_mat, cutree(cluster_fit, k=i), G2=T)
}

closeCluster(cl)

#	!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!	#
#	!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!	#
#	!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!	#
#	!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!	#

#extract indices of interest, that is a list of names as returned by the function
# "cluster.stat", see the help for more information

indices=c("cluster.number", "dunn", "dunn2", "g2")

cluster_performance=as.data.frame(t(cluster_groups[which(row.names(cluster_groups) 
			%in% indices==TRUE),]))

row.names(cluster_performance)=NULL

## save the R workspace as ".Rdata" in the working dir (optional of course)
save.image()
###############################################################################################
################################################################################################

<file>

doMPI provides a simple framework that allows running parallel applications almost no extra effort. However, this does not make sense at all time. Is the single iteration pretty fast, a serial implementation makes more sense because the communication overhead will slow down the entire process.

A more flexible framework is provided by the Rmpi implementation, which is not as easy the implement. A popular example can be found at at this page.