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April 09, 2010


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Hi David,
Is it possible to use doSMP package without installing the full installation exe ?

Tal, I don't think so, no. It's bundled and tested with the REvolution distribution.

Hi David,

I just made a simple code to compare foreach+doSMP on my dual core computer. And the performance was (much) slower then a simple for loop.
(Although looking at the CPU, I could see that doSMP did use both CPU's, which is cool)

Do you have any example showing superiority of doSMP with two cores, over the basic R code?
(since the difference might have had something to do with pre loading time of some sort)


Now that I am home, here is the code:

workers <- startWorkers(2)

sme <- matrix(rnorm(100), 10,10)

check <-function(n) {

times <- 100
system.time(x <- foreach(j=1:times ) %dopar% check(j))
system.time(for(j in 1:times ) x <- check(j))

Tal -

I haven't tried doSMP myself, yet, but I agree it will be a nice addition to the do* family.

Parallel computing isn't always faster. It's best for cases where the work being done at each step is substantial, so it isn't overwhelmed by the communication overhead. So, for example, inside your
check() function, you might do the solve 1000 times on 1000 random matrices.


Hi Jay,

I tried your suggestion - and it indeed works better with dopar.


David, is there any hope doSMP will eventually make it to CRAN?

Hi etn, I expect it will eventually make its way to r-forge or CRAN, but for now it's exclusively available in the REvolution R distribution.

Hi Tal,

I suspect the issue here is that solve() is using multi-threaded computations, and you are *also* parallelizing the large-grain computations with doSMP. In effect, you are running 4 "threads" on 2 processors, which will lead to inefficiencies.

Try running


before you run the foreach loop with doSMP. This will constrain the solve() computations to one thread (processor), allowing each iteration of the foreach loop full use of one processor.

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