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An article on an R package for parallel computing:
``In this paper we present an R add-on package for parallel computing: R/parallel. To use it, the programmer does not need to change his algorithm nor install and maintain any additional software as the R/parallel package is self-contained and capable of using current multicore processor desktop computers. It easily and effectively enables the automatic parallelization of loops without data dependencies, thus bringing the benefits of parallel computing within the reach of any bioinformatician using R. Its design allows its direct use with current bioinformatics analysis tools such as R/qtl, MetaNetwork or affyGG for analysis of genome-wide gene expression data.''ARTICLE
Gonzalo Vera, Ritsert C. Jansen and Remo L. Suppi. 2008. R/parallel – speeding up bioinformatics analysis with R. BMC Bioinformatics 9:390. http://www.biomedcentral.com/1471-2105/9/390
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Discussion forums: Software: R/parallel
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Has anybody been able to get the R/parallel work
Submitted by
Penny
;
posted on
Tuesday, February 24, 2009
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I tried to run it on both linix station and windows. It seems to run oK. However, the run time is much longer than running on one cpu. Does anybody know what is the problem? My code is roughly like this. Can somebody help me with this? Thanks
bgSubtract.dym.p <- function (sL.bg, mL.bg, sL.tg, mL.tg, lRT, rRT, ppm, lowThr, ppmAdj,fac)
{
sL.bg <- as.matrix(sL.bg)
sL.tg <- as.matrix(sL.tg)
mL.bg <- as.matrix(mL.bg)
mL.tg <- as.matrix(mL.tg)
mL.tg2 <- NULL
if( "rparallel" %in% names( getLoadedDLLs()) )
{
runParallel( resultVar="mL.tg2", resultOp="c", nWorkers=2 )
}
else
{
for (i in 1:nrow(sL.tg))
{
.....
for (j in start:end)
{
........
}
mL.tg2 <- c(mL.tg2, mL.tg[start:end,2])
}
}
return(mL.tg2)
}
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