Hello Michael,
thanks for the answer, it looks like that the foreach package might do what I
want. Few comments though
The foreach loop asks for a way to combine results, which I do not want to have
any. AFter I load a dataset the subsequent function does plotting and save the
files as pdfs, nothing more.
What is the difference between %do% and %dopar%, they look actually the same.
I do not see to be anyway to contol the number of used cores, like set to use
only 4, or 8 or 16.
Regards
Alex
________________________________
From: R. Michael Weylandt <michael.weyla...@gmail.com>
Cc: R help <R-help@r-project.org>
Sent: Tuesday, May 15, 2012 8:00 AM
Subject: Re: [R] Simple parallel for loop
Take a look at foreach() and %dopar$ from the CRAN package foreach.
Michael
> Dear all,
> I am having a for loop that iterates a given number of measurements that I
> would like to split over 16 available cores. The code is in the following
> format
>
> inputForFunction<-expand.grid(caseList,filterList)
> for (i in c(1:length(inputForFunction$Var1))){#
> FileList<-GetFileList(flag=as.vector(inputForFunction$Var1[i]));
> print(sprintf("Calling the plotsCreate for %s
> and%s",as.vector(inputForFunction$Var1[i]),as.vector(inputForFunction$Var2[i])))
>
>
> plotsCreate(Folder=mainFolder,case=as.vector(inputForFunction$Var1[i]),DataList=FileList,DataFilter=as.vector(inputForFunction$Var2[i]))
> }
>
> as you can see after the inputForFunction is calculated then my code iterates
> over the available combinations of caseList and filterList. It would be
> great, without major changes, split these "tasks" to all the available
> processors.
>
> Is there some way to do that?
>
> Regards
> Alex
>
> [[alternative HTML version deleted]]
>
>
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>
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