Trying to get started with doParallel and foreach but no improvement

Question

I am trying to use the doParallel and foreach package but I'm getting reduction in performance using the bootstrapping example in the guide found here CRANpage.

library(doParallel)
library(foreach)
registerDoParallel(3)
x <- iris[which(iris[,5] != "setosa"), c(1,5)]
trials <- 10000
ptime <- system.time({
  r <- foreach(icount(trials), .combine=cbind) %dopar% {
    ind <- sample(100, 100, replace=TRUE)
    result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit))
    coefficients(result1)
    }
  })[3]
ptime

This example returns 56.87.

When I change the dopar to just do to run it sequentially instead of in parallel, it returns 36.65.

If I do registerDoParallel(6) it gets the parallel time down to 42.11 but is still slower than sequentially. registerDoParallel(8) gets 40.31 still worse than sequential.

If I increase trials to 100,000 then the sequential run takes 417.16 and the parallel run with 3 workers takes 597.31. With 6 workers in parallel it takes 425.85.

My system is

• Dell Optiplex 990

• Windows 7 Professional 64-bit

• 16GB RAM

• Intel i-7-2600 3.6GHz Quad-core with hyperthreading

Am I doing something wrong here? If I do the most contrived thing I can think of (replacing computational code with Sys.sleep(1)) then I get an actual reduction closely proportionate to the number of workers. I'm left wondering why the example in the guide decreases performance for me while for them it sped things up?

Answer 1

The underlying problem is that doParallel executes attach for every task execution on the workers of the PSOCK cluster in order to add the exported variables to the package search path. This resolves various scoping issues, but can hurt performance significantly, particularly with short duration tasks and large amounts of exported data. This doesn't happen on Linux and Mac OS X with your example, since they will use mclapply, rather than clusterApplyLB, but it will happen on all platforms if you explicitly register a PSOCK cluster.

I believe that I've figured out how to resolve the task scoping problems in a different way that doesn't hurt performance, and I'm working with Revolution Analytics to get the fix into the next release of doParallel and doSNOW, which also has the same problem.

You can work around this problem by using task chunking:

ptime2 <- system.time({
  chunks <- getDoParWorkers()
  r <- foreach(n=idiv(trials, chunks=chunks), .combine='cbind') %dopar% {
    y <- lapply(seq_len(n), function(i) {
      ind <- sample(100, 100, replace=TRUE)
      result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit))
      coefficients(result1)
    })
    do.call('cbind', y)
  }
})[3]

This results in only one task per worker, so each worker only executes attach once, rather than trials / 3 times. It also results in fewer but larger socket operations, which can be performed more efficiently on most systems, but in this case, the critical issue is attach.