# foreach parallel loop comes from "foreach" package
# we also need to load doParallel package to define a parallel backend
library(parallel)
library(foreach)
library(doParallel)


# Check how many cores are requested by the job
( ncores <- as.numeric( Sys.getenv("NSLOTS") ) )

# If code is executed interactively, set the number of cores manually
if ( is.na(ncores) ) ncores <- 1

# Define a sample dataset
set.seed(42)
df <- as.data.frame( 
  matrix(sample(1:100, 100000000, replace=TRUE), nrow=100000) )

# Create a cluster from CPU cores and define a parallel backend
cl <- makeCluster(ncores)      # from library(parallel)
registerDoParallel(cl)         # from library(doParallel)

# Execute a loop in parallel
res <- foreach(xc=df, .combine='c') %dopar% {

   # compute sum of each vector (column in the input data frame)
   sum(xc)
}

# Stop the cluster
stopCluster(cl)