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.gal...@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 <shotw...@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.gal...@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]] ______________________________________________ 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.
