which parallel routine

I'm looking for advice on which of the parallel systems to use.

Context: maximize a likelihood, each evaluation is a sum over a large number of subjects (>5000) and each of those per subject terms is slow and complex.

If I were using optim the context would be
  fit <- optim(initial.values, myfun, ?.)
  myfun <- function(params) {
       ? Do some initial setup?
       temp <- apply-in-parallel(id,  per-subject-eval-fun, p=params)
       unlist(temp)
}

  The use of mcapply seems like there would be a lot of overhead starting and stopping threads.   But none of the tutorials I've found addresses this particular question.  Both direct answers and pointers to other docs would be welcome.

Terry T.