[R-SIG-Mac] Multiple cores on a Mac

[Bryan Hanson]

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 <pdalgd at 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 <joe at 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.mes at cbs.dk  Priv: PDalgd at gmail.com
> 
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