[R] gc() and memory efficiency

[Prof Brian Ripley]

1) See ?"Memory-limits": it is almost certainly memory fragmentation.
You don't need to give the memory back to the OS (and few OSes actually do 
so).

2) I've never seen this running a 64-bit version of R.

3) You can easily write a script to do this.  Indeed, you could write an R 
script to run multiple R scripts in separate processes in turn (via
system("Rscript fileN.R") ).   For example. Uwe Ligges uses R to script 
building and testing of packages on Windows.

On Mon, 4 Feb 2008, Doran, Harold wrote:

> I have a program which reads in a very large data set, performs some 
> analyses, and then repeats this process with another data set. As soon 
> as the first set of analyses are complete, I remove the very large 
> object and clean up to try and make memory available in order to run the 
> second set of analyses. The process looks something like this:
>
> 1) read in data set 1 and perform analyses
> rm(list=ls())
> gc()
> 2) read in data set 2 and perform analyses
> rm(list=ls())
> gc()
> ...
>
> But, it appears that I am not making the memory that was consumed in 
> step 1 available back to the OS as R complains that it cannot allocate a 
> vector of size X as the process tries to repeat in step 2.
>
> So, I close and reopen R and then drop in the code to run the second 
> analysis. When this is done, I close and reopen R and run the third 
> analysis.
>
> This is terribly inefficient. Instead I would rather just source in the 
> R code and let the analyses run over night.
>
> Is there a way that I can use gc() or some other function more 
> efficiently rather than having to close and reopen R at each iteration?
>
> I'm using Windows XP and r 2.6.1
>
> Harold
>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at 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.
>

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595