Hello Gabor, Matt, Dirk.

Thank you all for clarifying the situation.

So if I understand correctly then:
1) Changing the BLAST would require specific BLAST per computer
configuration (OS/chipset).
2) The advantage would be available only when doing  _lots_ of linear
algebra


So I am left wondering for each item:
1) How do you find a "better" (e.g: more suited) BLAST for your system? (I
am sure there are tutorials for that, but if someone here has
a recommendation on one - it would be nice)
2) In what situations do we use __lots" of linear algebra?  For example, I
have cases where I performed many linear regressions on a problem, would
that be a case the BLAST engine be effecting?
I am trying to understand if REvolution emphasis on this is a
marketing gimmick, or are they insisting on something that some R users
might wish to take into account.  In which case I would, naturally (for many
reasons), prefer to be able to tweak the native R system instead of needing
to work with REvolution distribution.

Lastly, following on Matt suggestion, if any has a tutorial on the subject,
I'd be more then glad to publish it on r-statistics/r-bloggers.

Thanks again to everyone for the detailed replies.

Best,
Tal




----------------Contact
Details:-------------------------------------------------------
Contact me: Tal.Galili@gmail.com |  972-52-7275845
Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
www.r-statistics.com (English)
----------------------------------------------------------------------------------------------




On Sat, Jun 12, 2010 at 6:01 AM, Matt Shotwell <shotwelm@musc.edu> wrote:

> In the case of REvolution R, David mentioned using the Intel MKL,
> proprietary library which may not be distributed in the way R is
> distributed. Maybe REvolution has a license to redistribute the library.
> For the others, I suspect Gabor has the right idea, that the R-core team
> would rather not keep architecture dependent code in the sources,
> although there is a very small amount already (`grep -R __asm__`).
>
> However, I know using Linux (Debian in particular) it is fairly
> straightforward to build R with `enhanced' BLAS libraries. The R
> Administration and Installation manual has a pretty good section on
> linking with enhanced BLAS and LAPACK libs, including the Intel MKL, if
> you are willing cough up $399, or swear not to use the library
> commercially or academically.
>
> Maybe a short tutorial using free software, such as ATLAS would be
> suitable content for an r-bloggers post :) ?
>
> Matt Shotwell
> Graduate Student
> Div. Biostatistics and Epidemiology
> Medical University of South Carolina
>
> On Fri, 2010-06-11 at 19:21 -0400, Tal Galili wrote:
> > Hello all,
> > I came across<
> http://www.r-bloggers.com/performance-benefits-of-linking-r-to-multithreaded-math-libraries/
> >
> > David
> > Smith's new post
> > Performance benefits of linking R to multithreaded math
> > libraries<
> http://blog.revolutionanalytics.com/2010/06/performance-benefits-of-multithreaded-r.html
> >
> > Which explains how (and why) REvolution distribution of R uses
> > different BLAS math libraries for R, so to
> > allow multi-threaded mathematical computation.
> > What the post doesn't explain is why it is that native R distribution
> > doesn't use the multi-threaded version of the libraries.  Is it because
> > R-devel team didn't get to it yet or is it for some technical reason.
> > Could someone please help to explain the situation?
> >
> > Thanks in advance,
> > Tal
> >
> > p.s: I wasn't sure if to send the question here or to R-devel, I decided
> to
> > send it here.  If I am in the wrong - please let me know.
> >
> >
> >
> > ----------------Contact
> > Details:-------------------------------------------------------
> > Contact me: Tal.Galili@gmail.com |  972-52-7275845
> > Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
> > www.r-statistics.com (English)
> >
> ----------------------------------------------------------------------------------------------
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org 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.
>
>

	[[alternative HTML version deleted]]

