I am interested in finding the answer. Once I get back to the simulation portion of my project I might squeeze some time into to do the experiments.

As a matter of fact, you don’t need RevoR to run the example I gave you. Base R plus ‘multicore’ package (plus any extras you need for your simulation) is enough.

Unless, of course, you meant that you simply don’t have linux machine available…

For those of you that haven’t seen this yet, Ryan Rosario (@DataJunkie), gave a talk that is much better than anything that I’ve been able to do: http://www.bytemining.com/2010/07/taking-r-to-the-limit-part-i-parallelization-in-r/

Can you post a comment to this post when you post a new post on the 4 core, 8 core, and 10 core test? I’d like to know the results, thanks!

Changing from 8 to 4 would cut the performance roughly in half depending on the communication overhead; in this example it should be pretty close to half. I’ll run it when I do michal’s stuff and make a separate post.

It’s fast. I like it. Windows 7 works well; Ubuntu 10.04 is much faster. Depends on what you can afford and what you want I guess. Seriously though, check out the passmark site. It will help you judge how much processing power you are buying.

Sorry to bother you with such requests, but can you try your code with 8 cores and 4 cores, and see if there really is an advantage? If there is, try it with 10 cores specified. I’m convinced that specifying 8 cores can’t beat 4 since the computer is a quad-core. I hope to be proven otherwise (I’m interested because, as said before, I’m in the market for a laptop I can do parallel computing with R).

Also, although its a quad-core, the clock speed is at 1.8ghz. Can you comment on the system performance? Regular usage and when computing with R? I’d like to know whether 1.8ghz suffices or whether I should really go for something with a higher clock speed like 2.4ghz (quad). Thanks!

@michal
I’ll compare when I return from the IES conference and report back. Thanks for the sample to check with!

I would be curious how, performance-wise, your simulation would run using some other parallel framework in R. For example the ‘multicore’ package. You could do it with something like:

library(multicore)

# matrix of simulation conditions
conditions <- matrix(rnorm(900), 100, 3)

# single simulation run
doit <- function(i)
{
# vector of inputs
v <- conditions[i,]
# some dummy calculations
c( result1=(v[1] + v[2]) * v[3],
result2=(v[2] + v[3]) * v[1],
result3=(v[3] – v[2]) * v[1] )
}

# this gives a list with a component for every run,
# change mc.cores to 8 in your setup
l <- mclapply(seq(1, nrow(conditions)), doit, mc.cores=3)
# cbind it
r <- do.call("cbind", l)