I'm not sure what you mean by full code or the iteration. This uses foreach to parallelize the loops over different tuning parameters and resampled data sets.
The only way I could set to split up the parallelism is if you are fitting different models to the same data. In that case, you could launch separate jobs for each model. If the data is large and quickly read from disk, that might be better than storing it in memory and sequentially running models in the same script. We have decent sized machines here, so we launch different jobs per model and then parallelize each (even if it is using 2-3 cores it helps). Thanks, Max On Fri, Oct 28, 2011 at 10:49 AM, 1Rnwb <sbpuro...@gmail.com> wrote: > the part of the question dawned on me now is, should I try to do the parallel > processing of the full code or only the iteration part? if it is full code > then I am at the complete mercy of the R help community or I giveup on this > and let the computation run the serial way, which is continuing from past > sat. > Sharad > > -- > View this message in context: > http://r.789695.n4.nabble.com/help-with-parallel-processing-code-tp3944303p3948118.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > 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. > -- Max ______________________________________________ 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.