Here's what I do to get processors on my Windows machine (it should work
for dual cores as well) fully engaged in a calculation (wow I like that)
require(snow)
require(doSNOW)
require(foreach)
#change the 8 to however many cores/phys processors you have on your machine
cl.tmp = makeCluster(rep("localhost",8), type="SOCK")
registerDoSNOW(cl.tmp)
foreach(dat=x) %dopar% someFunction(dat)
HTH
-c
On 6/16/2010 8:51 AM, Mario Valle wrote:
On 15-Jun-10 17:07, Sergey Goriatchev wrote:
Hello,
I am reading "Using The foreach Package" document and I have tried the
following:
---------------------------------------------------------------------
sessionInfo()
R version 2.10.1 (2009-12-14)
i386-pc-mingw32
locale:
[1] LC_COLLATE=German_Switzerland.1252
LC_CTYPE=German_Switzerland.1252
LC_MONETARY=German_Switzerland.1252 LC_NUMERIC=C
LC_TIME=German_Switzerland.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] foreach_1.3.0 codetools_0.2-2 iterators_1.0.3
x<- numeric(10000)
system.time(for(i in 1:10000) x[i]<- sqrt(i))
user system elapsed
0.03 0.00 0.03
system.time(system.time(x<- foreach(i=1:10000, .combine="c") %do%
sqrt(i)))
user system elapsed
7.14 0.00 7.14
system.time(system.time(x<- foreach(i=1:10000, .combine="c") %dopar%
sqrt(i)))
user system elapsed
7.19 0.00 7.19
Warning message:
executing %dopar% sequentially: no parallel backend registered
------------------------------------------------------------------------
Not only is the sequential foreach much slower than the simple
for-loop (as least in this particular instance), but I am not quite
sure how to make foreach run parallel. Where would I get this parallel
backend?
Use doMPI and run R through mpirun (for example run on 8 cores):
mpirun -np 8 R --slave -f your-script.r
Hope it helps
mario
I looked at doMC and doRedis, but these do not run on
Windows, as far as I understand. And doSNOW is something to use when
you have a cluster, while I have a simple dual-core PC.
It is not really clear for how to make parallel computing work.
Please, help.
Regards,
Sergey
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and provide commented, minimal, self-contained, reproducible code.
