[R] Optimal platform for R

[John Maindonald]

I've found memory management sometimes problematic under Windows.
I've  a calculation that runs without difficulty in 512MB under Mac OS X
(10.3 or 10.4); I'd expect the same under Linux.  Under Windows
(XP professional) with 512MB, it requires a freshly booted system.
But maybe the new machines will have so much memory that memory
management will not be an issue.

John Maindonald             email: john.maindonald at anu.edu.au
phone : +61 2 (6125)3473    fax  : +61 2(6125)5549
Mathematical Sciences Institute, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.


On 10 Mar 2006, at 10:00 PM, r-help-request at stat.math.ethz.ch wrote:

> From: Prof Brian Ripley <ripley at stats.ox.ac.uk>
> Date: 10 March 2006 6:50:03 PM
> To: gwelleni at bidmc.harvard.edu
> Cc: r-help at stat.math.ethz.ch
> Subject: Re: [R] Optimal platform for R
>
>
> On Thu, 9 Mar 2006, gwelleni at bidmc.harvard.edu wrote:
>
>> I'm looking to buy a new desktop which will primarily be used for
>> analyses of large datasets (100s of MB). I've seen postings from  
>> several
>> years back re the 'optimal' platform for running R, but nothing more
>> recently.
>
> It is a subject which comes up every few months.  Many of the  
> developers are running dual (or dual-core) Opterons/Athlon 64s  
> under Linux these days.
>
>> Specifically, I want to know: 1) if I run R under Windows, does  
>> having a
>> dual-processor machine help speed things up? And 2) is it still true
>> that R performs about as well under Windows as Linux?
>
> Duncan Murdoch has already mentioned the 64-bit advantage if you  
> need large datasets, but there is also a speed penalty if you do  
> not.  Your description seems on the margins (depends how many 100s  
> and what the format is and what you want to do).  One advantage of  
> AMD64 Linux is that I can run either 32- or 64-bit versions of R  
> and choose to have speed or space for any given task.
>
> A dual processor will be of little help in running R faster.  R's  
> interpreter is single-threaded, and although you can get some  
> advantage in using multi-threaded BLAS libraries in large matrix  
> computations these are not readily available for R under Windows,  
> and the advantage is often small under Linux. Running two or more  
> instances of R will take advantage of dual processers, and I have  
> been running dual CPU machines for a decade.
>
> As for Windows vs Linux, R runs on the same hardware at about the  
> same speed when comparing the standard Windows build with a shared  
> library version on Linux (standard for e.g. the RH RPMs), but the  
> standard Linux build is 10-20% faster. For one set of comparisons see
>
> 	http://sekhon.berkeley.edu/macosx/
>
> -- 
> 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
>