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: [EMAIL PROTECTED] 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, [EMAIL PROTECTED] wrote: > From: Prof Brian Ripley <[EMAIL PROTECTED]> > Date: 10 March 2006 6:50:03 PM > To: [EMAIL PROTECTED] > Cc: r-help@stat.math.ethz.ch > Subject: Re: [R] Optimal platform for R > > > On Thu, 9 Mar 2006, [EMAIL PROTECTED] 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, [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@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html