Parallel computing when using ‘CORElearn' in R - r

Question

I’m using the ReliefF for feature selection (using the package called "CORElearn"). It worked very well before. But later on, I want to speed up my code. Since I have bootstrap in my code (each loop is doing exactly the same thing, including using ReliefF), so I’m using the package ‘parallel’ for parallel computing. But I realized that every time when it comes to the part of ReliefF, the code will just stuck there.
The related codes are as followings:
num.round <- 10 # number of rounds for bootstrap
rounds.btsp <- seq(1, num.round) # sequence of numbers for bootstrap, used for parallel computing
boot.strap <- function(round.btsp) {
## some codes using other feature selection methods
print('Finish feature selection using other methods') # I can get this output
# use ReliefF to rank the features
data.ref <- data.frame(t(x.train.resample), y.train.resample, check.names = F) # add the param to avoid changing '-' to '.'
print('Start using attrEval') # I’ll get this output, but then I'll get stuck here
estReliefF <- attrEval('y.train.resample', data.ref, estimator = 'ReliefFexpRank', ReliefIterations = 30)
names(estReliefF) <- fea.name # This command needs to be added because it's very annoying that 'attrEval' will change the '-' in the names to '.'
print('Start using estReliefF') # I’ll never get here
fea.rank.ref <- estReliefF[order(abs(estReliefF), decreasing = T)]
fea.rank.ref <- data.frame(importance = fea.rank.ref)
fea.rank.name.ref <- rownames(fea.rank.ref) # the ranked feature list for this round
return(fea.rank.name.ref)
}
results.btsp <- mclapply(rounds.btsp, boot.strap, mc.cores = num.round)
What I’m thinking now is that the function ‘attrEval’ will use multiple cores for parallel computing (I read that in the document: https://cran.r-project.org/web/packages/CORElearn/CORElearn.pdf). Then there will be somehow conflict with the parallel that I’m using. When I change ’num.round’ to 1, then there’s no problem running the code (but even I set it to 2, it won’t work).
The server that I'm using has 80 cores.
Is there a way to solve this? I'm thinking that shutting down the parallel computing for the function 'attrEval' maybe a solution? Even though I don't how to do that~~~

Answer 1

Having multiple levels of parallelism can be tricky. Unfortunately the CORElearn package does not allow to directly manipulate the number of threads used. Since it uses OpenMP for parallel execution, you can try to set the environment variable OMP_NUM_THREADS appropriately, e.g.
Sys.setenv(OMP_NUM_THREADS = 8)
num.round <- 10
This way there should be 10 groups of 8 cores, each group handling one bootstrap round.

Answer 2

Got a solution from the contributor of the package Marko: disable multithreading in CORElearn by using parameter maxThreads=1 in 'attrEval'