[R] CPU or memory

Wed Nov 8 19:10:57 CET 2006

Prof. Ripley,

Do you mind providing some pointers on how "coarse-grained parallelism"
could be implemented on a Windows environment?  Would it be as simple as
running two R-console sessions and then (manually) combining the results of
these simulations.  Or it would be better to run them as batch processes.
RSiteSearch('coarse grained') did not produce any hits so this topic might
have not been discussed on this list.

I am not really familiar with running R in any mode other than the default
(R-console in Windows) so I might be missing something really obvious. I am
interested in running Monte-Carlo cross-validation in some sort of a
parallel mode on a dual core (Pentium D) Windows XP machine.

Thank you.
-Christos

Christos Hatzis, Ph.D.
Nuvera Biosciences, Inc.
400 West Cummings Park
Suite 5350
Woburn, MA 01801
Tel: 781-938-3830
www.nuverabio.com

-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Prof Brian Ripley
Sent: Wednesday, November 08, 2006 5:29 AM
To: Stefan Grosse
Cc: r-help at stat.math.ethz.ch; Taka Matzmoto
Subject: Re: [R] CPU or memory

On Wed, 8 Nov 2006, Stefan Grosse wrote:

> 64bit does not make anything faster. It is only of use if you want to 
> use more then 4 GB of RAM of if you need a higher precision of your 
> variables
>
> The dual core question: dual core is faster if programs are able to 
> use that. What is sure that R cannot make (until now) use of the two 
> cores if you are stuck on Windows. It works excellent if you use 
> Linux. So if you want dual core you should work with linux (and then 
> its faster of course).

Not necessarily.  We have seen several examples in which using a
multithreaded BLAS (the only easy way to make use of multiple CPUs under
Linux for a single R process) makes things many times slower.  For tasks
that are do not make heavy use of linear algebra, the advantage of a
multithreaded BLAS is small, and even from those which do the speed-up is
rarely close to double for a dual-CPU system.

John mentioned simulations.  Often by far the most effective way to use a
multi-CPU platform (and I have had one as my desktop for over a decade) is
to use coarse-grained parallelism: run two or more processes each doing some
of the simulation runs.

> The Core 2 duo is the fastest processor at the moment however.
>
> (the E6600 has a good price/performance ration)
>
> What I already told Taka is that it is probably always a good idea to 
> improve your code for which purpose you could ask in this mailing 
> list... (And I am very sure that you have there a lot of potential).
> Another speeding up possibility is e.g. using the atlas library...
> (where I am not sure if you already use it)
>
> Stefan
>
> John C Frain schrieb:
>> *Can I extend Taka's question?*
>> **
>> *Many of my programs in (mainly simulations in R which are cpu bound) 
>> on a year old PC ( Intel(R) Pentium(R) M processor 1.73GHz or Dell 
>> GX380 with 2.8Gh Pentium) are taking hours and perhaps days to 
>> complete on a one year old PC.  I am looking at an upgrade but the 
>> variety of cpu's available is
>> confusing at least.   Does any one know of comparisons of the Pentium
>> 9x0, Pentium(r)
>> Extreme/Core 2 Duo,   AMD(r) Athlon(r) 64 , AMD(r) Athlon(r) 64
>> FX/Dual Core AM2 and
>> similar chips when used for this kind of work.  Does anyone have any 
>> advice on (1)  the use of a single core or dual core cpu or (2) on 
>> the use of 32 bit and 64 bit cpu.  This question is now much more 
>> difficult as the numbers on the various chips do not necessarily 
>> refer to the relative speed of the chips.
>> *
>> *John
>>
>> * On 06/11/06, Taka Matzmoto <sell_mirage_ne at hotmail.com> wrote:
>>
>>
>>> Hi R users
>>>
>>> Having both a faster CPU and more memory will boost computing power. 
>>> I was wondering if only adding more memory (1GB -> 2GB)  will 
>>> significantly reduce R computation time?
>>>
>>> Taka,
>>>
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>>
>>
>>
>>
>
> ______________________________________________
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> PLEASE do read the posting guide 
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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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