Knowledge Base

Using R and R Studio on RACC

The “parallel” library is used for parallel processing!

Uses an internal R dataset called MASS which has ‘Boston’ and ‘iris’ data.

This can be used in a batch script on the RACC.

'R script for parallelising.


starts <- rep(100, 40)

fx <- function(nstart) kmeans(Boston, 4, nstart=nstart)
numCores <- detectCores()

results <- lapply(starts, fx)

results <- mclapply(starts, fx, mc.cores = numCores)

x <- iris[which(iris[,5] != "setosa"), c(1,5)]
trials <- seq(1, 10000)

boot_fx <- function(trial) {
ind <- sample(100, 100, replace=TRUE)
result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit))
r <- coefficients(result1)
res <- rbind(data.frame(), r)
results <- mclapply(trials, boot_fx, mc.cores = numCores)

Output shows the processing speeds using the different libraries lapply and mclapply


RACC updates

We have changed the default partition to be the 'limited'. Users submitting jobs longer than 24h need to explicitly request another partition, e.g. 'cluster'. Jobs submitted with time limit exceeding the partition limit will be rejected.   Purchasing RACC nodes owned by projects can  be requested using the Self Service Portal form,  see /
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