foreach/doParallel/PSOCK/data.table benchmark V2

suppressMessages(library(doParallel))
library(itertools)
library(data.table)

args <- commandArgs(trailingOnly=TRUE)
n <- if (length(args) > 0) as.integer(args[1]) else 1000000
m <- if (length(args) > 1) as.integer(args[2]) else ceiling(n / 100)
set.seed(107)
td <- data.table(val=rnorm(n), id=sample(m, n, replace=TRUE))

cl <- makePSOCKcluster(4)
registerDoParallel(cl)
workers <- getDoParWorkers()

vadd <- function(a, ...) {
  for (v in list(...))
    a <- a + v
  a
}
cmean <- function(v, mine) if (mine) mean(v) else 0

start <- proc.time()[3]
nuniq <- length(unique(td$id))
res <- foreach(grps=isplitIndices(nuniq, chunks=workers),
               .combine='vadd',
               .multicombine=TRUE,
               .inorder=FALSE,
               .packages='data.table') %dopar% {
  td[, means := cmean(td$val[-.I], .GRP %in% grps), by=id]
  td$means
}
elapsed <- proc.time()[3] - start

cat(sprintf("foreach/doParallel/PSOCK/data.table with %d workers:\n", workers))
cat(sprintf("Rows: %d, Unique IDs: %d\n", length(td$id), length(unique(td$id))))
cat(sprintf("Elapsed time: %f\n", elapsed))

library(digest)
cat(sprintf("MD5 hash: %s\n", digest(res)))

stopCluster(cl)