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

[peter dalgaard]

Right, thanks. 

Digging back in the archives, the story was that there used to be a libRblas.veclib.dylib to link to and _that_ disappeared for some reason, but linking to
 
/System/Library/Frameworks/Accelerate.framework/......../libBLAS.dylib

has worked all along.

Interestingly, this transpires from a discussion involving Michael Braun and Simon Urbanek on R-sig-mac on Nov.20-23 2014, while a complaint by Joseph Kunkel, about the FAQ recipe not working, on Nov.23 2014 went uncommented....

Reading the old threads also suggests that the intention was to either reinstate libRblas.veclib.dylib or update the FAQ, but neither seems to have happened.

-pd

On 05 Feb 2016, at 16:20 , Bryan Hanson <hanson at depauw.edu> wrote:

> 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
>> 
>> _______________________________________________
>> R-SIG-Mac mailing list
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> 

-- 
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