[Rd] Memory question on R
roger.bos at gmail.com
Fri May 20 15:35:56 CEST 2005
I don't know about hyperthreading, but on my 4GB XP machine I can read
in very large data files to the extend that Windows Task Manager shows
the Rgui is using 2.3 to 2.7 GB. Further, I can run automated
simulations on this data all weekend with the processor at 99% without
the system or R crashing. But to do this I had to modify the header
file to make Rgui /LARGEADDRESSAWARE. There is a FAQ which describes
how to do this. Let me know if you cannot find it.
However, it may be worth checking your code and seeing if there is
anyway you can avoid recursiveness as this may be not memory
This is definitely a R-help questions. Rd is for bug reports and such.
On 5/20/05, Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote:
> On Fri, 20 May 2005, Dr L. Y Hin wrote:
> > Dear all,
> > Apology for posting this amateurish question.
> > I am running R version 2.1.0 on Windows XP for a simulation exercise.
> > Seemingly, I've encountered memory allocation problem during the
> > recursive procedure.
> > I've looked at the help section called
> > ?Memory() and ?memory.limit(), and ?memory.size but the comamands
> > seem to refer to Unix version of R.
> If you looked in a Windows version of R they do not (and they should not
> have the ()).
> Please do read the posting guide and (as we ask there) show us exactly
> what the messages you got were. This question seems more appropriate to
> R-help than R-devel.
> > I wound be very grateful if anyone could kindly advise me on the method
> > to set the memory allocation so that R workspace can harness the maximum
> > resources available on the hardware (RAM and virtual memory), with an understanding
> > that my machine may not have sufficient resources to run other applications.
> > In addition, does R version 2.1.0 makes use of the hyperthreading feature that may
> > speed up the application?
> > Thanking you in advance
> > Lin
> > [[alternative HTML version deleted]]
> We do ask for no HTML mail.
> 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
> R-devel at stat.math.ethz.ch mailing list
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