[R] Powerful PC to run R

[Prof Brian Ripley]

On Sun, 15 May 2011, Duncan Murdoch wrote:

> On 15/05/2011 3:02 PM, Aram Fingal wrote:
>> 
>> On May 13, 2011, at 6:38 AM, Michael Haenlein wrote:
>> 
>>> Dear all,
>>> 
>>> I'm currently running R on my laptop -- a Lenovo Thinkpad X201 (Intel Core
>>> i7 CPU, M620, 2.67 Ghz, 8 GB RAM). The problem is that some of my
>>> calculations run for several days sometimes even weeks (mainly simulations
>>> over a large parameter space). Depending on the external conditions, my
>>> laptop sometimes shuts down due to overheating.
>> 
>> 
>> You didn't mention whether you are using a 64-bit OS or not.  A single 
>> 32-bit process can not use more than 2 GB RAM.

And that is also false.  For Windows, see the rw-FAQ.  It is 
address space (not RAM) that is limited, and it is limited to 4GB *by 
definition* in a 32-bit process.  Many OSes can give your process 4GB 
of address space, but may reserve some of it for the OS.

>>  If your calculations would 
>> benefit from the full 8 GB RAM on your machine, you need to be able to run 
>> 64-bit R.   My understanding is that, on Windows, you either have to 
>> install the OS as 32-bit and use all 32-bit software or install 64-bit 
>> Windows and run all 64-bit software.  A Mac can run 32-bit and 64-bit 
>> software simultaneously and I'm not sure about Linux.  In the case of 
>> Linux, it probably doesn't matter so much because most Linux software is 
>> available as open source and you can compile it yourself either way.

For the record, all modern 64-bit OSes on x86_64 cpus can do this 
provided you install 32-bit versions of core dynamic libraries.  I run 
32- and 64-bit R on 64-bit Linux, Solaris, FreeBSD, Darwin (the OS of 
Mac OS X), Windows ....  As can AIX and IRIX on their CPUs.

> No, 64 bit Windows can run either 32 or 64 bit Windows programs.
>> 
>>> 
>>> I'm now thinking about buying a more powerful desktop PC or laptop. Can
>>> anybody advise me on the best configuration to run R as fast as possible? 
>>> I
>>> will use this PC exclusively for R so any other factors are of limited
>>> importance.
>> 
>> You need to evaluate whether RAM or raw processor speed is most critical 
>> for what you're doing.  In my case, I upgraded my Mac Pro to 16 GB RAM and 
>> was able to do hierarchical clustering heatmaps overnight which previously 
>> took more than a week to compute.  Using the Activity Monitor utility, it 
>> looks like some of the, even larger, heatmap computations would benefit 
>> from 32 GB RAM or more.
>> 
>> Linux runs on the widest range of hardware and that allows you the greatest 
>> ability to shop around.  If RAM is the deciding factor, then you can look 
>> around for a machine which can hold as much RAM as possible.  If processor 
>> speed is the factor, then you can optimize for that.  Windows runs on a 
>> reasonable array of hardware but has the disadvantage that the OS, itself, 
>> uses a lot of resources.

Nothing like as much as Mac OS X, though.  (I would say the main 
disadvantage of Windows for R is the slowness of the file systems.)

>> The Mac has the advantage of flexibility.  When you download the 
>> precompiled R package, it comes with both a 32-bit and a 64-bit executable. 
>> This is because 32-bit processes run a little faster if you don't need 
>> large amounts of RAM.  If you do need the RAM, then you run the 64-bit 
>> version.
>> 
>
> The same is true for Windows binaries on CRAN.

And of e.g. the Fedora binaries.

> Duncan Murdoch
>
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html

Mr Fingal: please do!  You are clearly unfamiliar with the R manuals.

-- 
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