This is not sound advice. For 1GB yes, perhaps 2GB. Beyond that the
extra freedom in the address space of a 64-bit system pays off.
The user address space of a 32-bit Linux system is (in the examples I have
seen) 3 to 3.5Gb. See ?"Memory-limits" for why that is restrictive.
There are some anomalies, depending on the CPU. On Intel Core 2 Duos
manipulating 64-bit pointers seems to be as efficient as 32-bit ones and
on some platforms (e.g. Mac OS 10.5.3) 64-bit is actually faster than
32-bit R. So very similar CPUs can give quite different performance
differences with 32- vs 64-bit R.
On Fri, 6 Jun 2008, Roland Rau wrote:
Dear all,
a related follow up -- with the hope for some feedback from the specialists.
Is the following general advice justified:
=========================================================
If one has not more than 4GB RAM and one wants to run primarily R on one's
Linux machine, it is a good idea to install the 32bit version of the
operating system.
The reasons are:
The machine has 4GB RAM which implies that the 32bit version can
(theoretically) use the whole available memory address space. The advantage
of addressing more memory using 64bit is in this instance of a 4GB computer
lost. Furthermore, 64bit often runs slower than 32bit (see Section 8 of R
Admin Manual) due to the larger pointer size.
=========================================================
Thanks,
Roland
steven wilson wrote:
Dear all;
I'm planning to install Linux on my computer to run R (I'm bored of
W..XP). However, I haven't used Linux before and I would appreciate,
if possible, suggestions/comments about what could be the best option
install, say Fedora, Ubuntu or OpenSuse which to my impression are the
most popular ones (at least on the R-help lists). The computer is a PC
desktop with 4GB RAM and Intel Quad-Core Xeon processor and will be
used only to run R.
Thanks
Steven
______________________________________________
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.
--
Brian D. Ripley, [EMAIL PROTECTED]
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-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.
