test_mclapply.R

library(parallel)

## small/large obviously depend on the OS and memory available
n_small <- 1e2
n_large <- 1e4

## example from ?svd
hilbert <- function(n) { i <- 1:n; 1 / outer(i - 1, i, "+") }
my_svd <- function(..., n){
  X <- hilbert(n)[, 1:6]
  s <- svd(X)
  return(TRUE)
}

mclapply(1:2, my_svd, n = n_small, mc.cores = 1) # works fine
mclapply(1:2, my_svd, n = n_small, mc.cores = 2) # works fine
mclapply(1:2, my_svd, n = n_large, mc.cores = 1) # works fine
mclapply(1:2, my_svd, n = n_large, mc.cores = 2) # returns a list of NULL

The multisession version definitely works, but I wondered about forked workers on linux/mac.

The multisession version definitely works, but I wondered about forked workers on linux/mac.

multicore (!= multisession) workers (e.g. plan("multicore")) use forked processes utilizing the same framework as parallel::mclapply() does internally.