I haven't actually used foreach very much myself, but I would imagine
that you could just take advantage of the fact that most plot
functions return their arguments silently and then just throw the
results away (i.e., don't assign them)
Switching %do% to %dopar% automatically activates parallelization
(dopar being "do in parallel")
I believe you decide the number of cores to use when you set up your
parallel backend (either multicore or snow)
Hope this helps,
Michael
On Tue, May 15, 2012 at 2:20 AM, Alaios <ala...@yahoo.com> wrote:
> 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>
> To: Alaios <ala...@yahoo.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
>
> On Tue, May 15, 2012 at 1:57 AM, Alaios <ala...@yahoo.com> wrote:
>> 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]]
>>
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
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