How to check if %dopar% really run parallel?

Well, typically you can tell because all your CPU's go to 100% on your system 
monitor(s).

This is what is called a 'toy example'.  The vignettes for foreach go over the 
fact that there is no free lunch, and for parallelism to make sense, you need 
to minimize communication costs between the master and slave nodes.  In your 
example, the communication cost, and the cost of collecting the data, will be 
larger than the cost of doing the calculation, making parallelism actually 
detrimental.  A recent discussion on 'chunking' on this list should provide 
pointers on what you can do when your application involves a large number of 
very-small calculations to minimize communication overhead.

While I must assume that your actual problem is more complex than replicating 
the same number one million times, I suggest you (re)read the vignettes that 
come with foreach.  I also suggest that when you post next, tell the list a 
little more about your actual application that you wish to parallelize, as this 
should allow the list to give you better guidance.

Regards,

   - Brian

Is there any way to check that %dopar% really runs parallel?
The following code (on a dual core laptop running windows+R 2.11.0pat 
and on Linux+R2.11.0) runs %dopar% more slowly than the same %do% code.
BTW, if you see any obvious mistake in the code...
Thanks!
        mario


library(doSNOW)
library(foreach)

fun <- function() for(q in 1:1000000) sqrt(3)

system.time(times(10000) %do% fun, gcFirst = TRUE)
#   user  system elapsed
#   5.74    0.01    6.24

cl <- makeCluster(2, type = "SOCK")
registerDoSNOW(cl)

system.time(times(10000) %dopar% fun, gcFirst = TRUE)
#   user  system elapsed
#   7.89    0.19    9.01

stopCluster(cl)

Brian G. Peterson
http://braverock.com/brian/
Ph: 773-459-4973
IM: bgpbraverock