I would like to thank all who replied to my question about the efficiency of
various cpu's in R.
Following the advice of Bogdan Romocea I have put a sample simulation and
the latest version of R on a USB drive and will go to a few suppliers to try
it out. I will report back if I find anything of interest.
With regard to 64-bit and 32-bit I thought that the 64-bit chip might
require less clock cycles for a specific machine instruction than a 32-bit.
This was one of the advantages of moving from 8 to 16 or from 16 to 32 bit
chips. Thus a slower, in terms of clock speed, 64-bit chip might run faster
than a somewhat similar 32-bit chip. I fully realize that the full
advantage of a 64-bit chip is available only with a 64-bit operating system
and I am preparing to switch some work to Linux in case I acquire a 64-bit
PC. If I do I will time the simulations on that also.
I already do some "coarse-grained parallelism" as described by *Brian Ripley
* but on two separate PC's. This is not ideal but allows the processing
time to be halved without the overheads.
FORTRAN 2 was my first programming language and I agree that I should try to
use C or FORTRAN to speed up things. Finally Rprof could be a great help.
There are lots of utilities in the utils package with which I was not
familiar.
Again Many Thanks to all who made various suggestions.
bogdan romocea <[EMAIL PROTECTED]> to *r-help*, me
More options 07-Nov (1 day ago) > 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.
On 08/11/06, Prof Brian Ripley <[EMAIL PROTECTED]> wrote:
>
> On Wed, 8 Nov 2006, Christos Hatzis wrote:
>
> > 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.
>
> That is what I would do in any environment (I don't do such things under
> Windows since all my fast machines run Linux/Unix).
>
> Suppose you want to do 10000 simulations. Set up two batch scripts
> that each run 5000, and save() the results as a list or matrix under
> different names, and set a different seed at the top. Then run each via
> R CMD BATCH simultaneously. When both have finished, use an interactive
> session to load() both sets of results and merge them.
>
> > 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: [EMAIL PROTECTED]
> > [mailto:[EMAIL PROTECTED] On Behalf Of Prof Brian Ripley
> > Sent: Wednesday, November 08, 2006 5:29 AM
> > To: Stefan Grosse
> > Cc: [email protected]; 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 <[EMAIL PROTECTED]> 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,
> >>>>
> >>>> _________________________________________________________________
> >>>> Get FREE company branded e-mail accounts and business Web site from
> >>>> Microsoft Office Live
> >>>>
> >>>> ______________________________________________
> >>>> [email protected] mailing list
> >>>> https://stat.ethz.ch/mailman/listinfo/r-help
> >>>> PLEASE do read the posting guide
> >>>> http://www.R-project.org/posting-guide.html
> >>>> and provide commented, minimal, self-contained, reproducible code.
> >>>>
> >>>>
> >>>
> >>>
> >>>
> >>>
> >>
> >> ______________________________________________
> >> [email protected] mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-help
> >> PLEASE do read the posting guide
> >> http://www.R-project.org/posting-guide.html
> >> and provide commented, minimal, self-contained, reproducible code.
> >>
> >
> >
>
> --
> 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
>
> ______________________________________________
> [email protected] mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
John C Frain
Trinity College Dublin
Dublin 2
Ireland
www.tcd.ie/Economics/staff/frainj/home.html
mailto:[EMAIL PROTECTED]
mailto:[EMAIL PROTECTED]
[[alternative HTML version deleted]]
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
