Parallel processing in R limited

Question

I\'m running ubuntu 12.04 and R 2.15.1 with the parallel and doParallel packages. when I run anything in parallel I\'m limited to 100% of a core, when I should have up to 80

Answer 1

My guess is that you probably had the wrong code. I would like to post one example copied from online http://www.r-bloggers.com/parallel-r-loops-for-windows-and-linux/ :

library(doMC)
registerDoMC()
x<- iris[which(iris[,5]!='setosa'),c(1,5)]
trials<- 10000
r<- foreach(icount(trials), .combine=cbind) %dopar% {
    ind<- sample(100,100,replace=T)
    result1<- glm(x[ind,2]~x[ind,1],family=binomial(logit))
    coefficients(result1)
}

and you can define how many cores you want to use in the parallel:

options(cores=4)

Answer 2

The problem may be that the R process is restricted to one core (and the subprocesses inherit that).

Try this:

> system(sprintf("taskset -p 0xffffffff %d", Sys.getpid()))
pid 3064's current affinity mask: fff
pid 3064's new affinity mask: fff

Now, if on your machine, the current affinity mask reports a 1, then this was the problem. The line above should solve it (i.e. the second line should report fff (or similar).

Simon Urbanek wrote a function mcaffinity that allows this control for multicore. As far as I know, it's still in R-devel.

For details, see e.g. this discussion on R-sig-hpc.

Update, and addition to Xin Guo's answer:

If you use implicit parallelization via openblas and explicit parallelization (via parallel/snow/multicore) together, you may want to change the number of threads that openblas uses depending on whether you are inside an explicitly parallel part or not.
This is possible (with openblas under Linux, I'm not aware of any other of the usual optimized BLAS' that provides a function to the number of threads), see Simon Fuller's blog post for details.

Answer 3

I experienced the same problem because of the libblas.so(.3gf) packages, and I do not know if this also causes your problem. When R starts it calls a BLAS system installed in your system to conduct linear algebraic computations. I have libopenblas.so(.3gf) and it is highly optimized with the option "CPU Affinity", that is to say, when you do numerical vector or matrix computation, the openblas package will just make 8 threads and make each one of the threads stuck to one specified and fixed CPU to speed up the codes. However, by setting this, your system is then told that all the CPU's are very busy, and thus if further parallel tasks come, the system will try to squeeze them into one CPU so as to try best not to interfere the busy CPU's.

So this was my solution which worked: I downloaded an openblas package source and compiled it with the file "Makefile.rule" changed: there is one line "#NO_AFFINITY = 1" and I just deleted "#" so that after compiled, there is no affinity option selected. Then I installed the package and the problem was solved.

For the reference of this, see https://github.com/ipython/ipython/issues/840

Please note that this is a trade-off. Removing CPU affinity will make you lose some efficiency when doing numerical computations, that's why though the openblas maintainer (Dr. Xianyi Zhang) knows the problem, he still publish the codes with the cpu affinity as a default option.