library(parallel)


# 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 function to be executed by each core
myFun <- function(x){ sum(x ) }

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

# Create a Parallel Socket Cluster from multiple CPU cores
cl <- makeCluster( ncores )

    # Execute function in parallel
    res <- clusterApply( cl, x = df, fun = myFun)

# Stop the cluster 
stopCluster(cl)