On El Cap you can still pull off the symlink approach, as described here, without building R fresh:
http://blog.quadrivio.com/2015/06/improved-r-performance-with-openblas.html The next time you launch R you will have the library you specified. Bryan > On Feb 5, 2016, at 10:03 AM, peter dalgaard <pda...@gmail.com> wrote: > > Why me..? > > Probably Simon and maybe Brian has the full recollection of the story, but as > I understood it, once upon a time you could switch out the BLAS on CRAN R > just by editing a symlink in the R installation. For some reason, that cannot > work anymore (Apple considered symlinked libraries a security risk?). I think > the current situation is that you can still do it, but you have to physically > overwrite the default BLAS(? Simon will know.). > > At any rate, you can certainly still do a local compile with the Accelerate > framework, once you get all the tools in place: > >> M <- matrix(rnorm(1e8),10000) >> system.time(M %*% t(M)) > user system elapsed > 219.566 0.806 21.752 >> M <- crossprod(M) >> system.time(solve(M)) > user system elapsed > 310.874 1.477 29.998 > > (To tell the truth, I actually don't have all the tools in place on that > machine, so this was from a build of 3.2.1 patched) > > -pd > > On 05 Feb 2016, at 14:52 , Joseph Kunkel <j...@bio.umass.edu> wrote: > >> To me there are big gorillas in the room and I need to know why I can not >> use them all. >> >> • Testing for physical and logical cpus on Joe's MacBook Pro. >> Josephs-MacBook-Pro:~ josephgkunkel$ /usr/sbin/sysctl -n hw.physicalcpu >> 4 >> Josephs-MacBook-Pro:~ josephgkunkel$ /usr/sbin/sysctl -n hw.logicalcpu >> 8 >> >> Prior to about 2012 my multicore Macs would use all (physical) cores >> automagically in R. %*% was multicore automatically. >> >> A 24 hour heavy matrix calculation would take a little over 6 hours on a 4 >> core Mac. >> >> Then some problem with the BLAS library changed everything and colleague >> stats people and I got really mad that we could not do our calculations as >> fast in R. >> >> Many work-around libraries were devised which did not seem to be that useful >> for freewielding matrix operations. >> >> Then Revolution R seemed to solve the problem and patented(?) it. … but not >> for Macs. >> >> Recently they provided a free Mac version but using their R ‘open' version >> messes up my computer for updating the libraries I am addicted to using. >> >> My question after this appologeticlay long narrative is: >> >> Why has no satisfactory and transparent method for multicore use been >> available to CRAN R? >> >> Secondarily, how could our R open software system be hijacked by Revolution >> and now Microsoft? >> >> I would be pleased to know that there are colleagues out there who are >> similarly hoping for an R core solution within CRAN. >> >> I can do primitive matrix things faster with Julia, which is encouraging, >> but the libraries and flexability for me are not there yet. >> >> Joe >> >> -·. .· ·. .><((((º>·. .· ·. .><((((º>·. .· ·. .><((((º> .··.· >=- >> =º}}}}}>< >> Joseph G. Kunkel, Research Professor >> 112A Marine Science Center >> University of New England >> Biddeford ME 04005 >> http://www.bio.umass.edu/biology/kunkel/ >> >> >> > > -- > Peter Dalgaard, Professor, > Center for Statistics, Copenhagen Business School > Solbjerg Plads 3, 2000 Frederiksberg, Denmark > Phone: (+45)38153501 > Office: A 4.23 > Email: pd....@cbs.dk Priv: pda...@gmail.com > > _______________________________________________ > R-SIG-Mac mailing list > R-SIG-Mac@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-mac _______________________________________________ R-SIG-Mac mailing list R-SIG-Mac@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-mac