[R] R + Linux

Prof Brian Ripley ripley at stats.ox.ac.uk
Fri Jun 6 23:09:10 CEST 2008

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
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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

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