Ashamed at my lack of parallel programming, I decided to learn some R Parallel Programming (after all parallel blogging is not really respect worthy in tech-geek-ninja circles).
So I did the usual Google- CRAN- search like a dog thing only to find some obstacles.
Obstacles-
Some Parallel Programming Packages like doMC are not available in Windows
http://cran.r-project.org/web/packages/doMC/index.html
Some Parallel Programming Packages like doSMP depend on Revolution’s Enterprise R (like –
http://blog.revolutionanalytics.com/2009/07/simple-scalable-parallel-computing-in-r.html
and http://www.r-statistics.com/2010/04/parallel-multicore-processing-with-r-on-windows/ (No the latest hack didnt work)
or are in testing like multicore (for Windows) so not available on CRAN
http://cran.r-project.org/web/packages/multicore/index.html
fortunately available on RForge
http://www.rforge.net/multicore/files/
Revolution did make DoSnow AND foreach available on CRAN
see http://blog.revolutionanalytics.com/2009/08/parallel-programming-with-foreach-and-snow.html
but the documentation in SNOW is overwhelming (hint- I use Windows , what does that tell you about my tech acumen)
http://sekhon.berkeley.edu/snow/html/makeCluster.html and
http://www.stat.uiowa.edu/~luke/R/cluster/cluster.html
what is a PVM or MPI? and SOCKS are for wearing or getting lost in washers till I encountered them in SNOW
Finally I did the following-and made the parallel programming work in Windows using R
require(doSNOW)
cl<-makeCluster(2) # I have two cores
registerDoSNOW(cl)
# create a function to run in each itteration of the loop
check <-function(n) {
+ for(i in 1:1000)
+ {
+ sme <- matrix(rnorm(100), 10,10)
+ solve(sme)
+ }
+ }
times <- 100 # times to run the loop
system.time(x <- foreach(j=1:times ) %dopar% check(j))
user system elapsed
0.16 0.02 19.17
system.time(for(j in 1:times ) x <- check(j))
user system elapsed</pre>
39.66 0.00 40.46
stopCluster(cl)
And it works!
DECISION STATS 