Job script:
#!/bin/bash
#SBATCH --job-name=rfee
#SBATCH --workdir=/home/user.name/rfee/
#SBATCH --output=r_foreach_example_console_output.txt
#SBATCH --mem-per-cpu=100 # specify RAM per CPU here in Mb
#SBATCH --time=0:02:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4 # specify number of CPUs to use here
module load r-3.5.1-gcc-4.8.5-esnjoca
R --file=./r_foreach_example.R
R Script (r_foreach_example.R):
# specify the path from which to load R packages
.libPaths('/home/user.name/R')
# load the packages:
library('doMC') # note: loading 'doMC' also loads 'foreach'
# a simple function to execute first in serial then again in parallel
f1 <- function(x){
Sys.sleep(2)
return(x)
}
# import the number of available CPUs
# this is the number we supplied at the line
#SBATCH --cpus-per-task=4
# in our .sh file
n.cpus <- Sys.getenv("SLURM_CPUS_PER_TASK")
n.cpus
class(n.cpus)
# we need this to be numeric below so:
n.cpus <- as.numeric(n.cpus)
n.cpus
class(n.cpus)
# register a parallel backend specifying the number of CPUs as the number we imported via Sys.getenv()
registerDoMC(cores = n.cpus)
# run a serial foreach loop
system.time(
s1 <- foreach(i = 1:4, .combine = c) %do%
f1(i)
)
# run a parallel foreach loop
system.time(
s2 <- foreach(i = 1:4, .combine = c) %dopar%
f1(i)
)
# the parallel foreach loop should be faster