R help - Nov 2024 - [EXT] Mac ARM for lm() ?

Not a direct answer but you may find lm.fit worth experimenting with.

Also try the high-performance computing task view on CRAN

Cheers,

Andrew

--
Andrew Robinson
Chief Executive Officer, CEBRA and Professor of Biosecurity,
School/s of BioSciences and Mathematics & Statistics
University of Melbourne, VIC 3010 Australia
Tel: (+61) 0403 138 955
Email: apro at unimelb.edu.au
Website: https://researchers.ms.unimelb.edu.au/~apro at unimelb/

I acknowledge the Traditional Owners of the land I inhabit, and pay my respects
to their Elders.
On 14 Nov 2024 at 1:13?PM +0100, Ivo Welch <ivo.welch at gmail.com>,
wrote:
External email: Please exercise caution

I have found more general questions, but I have a specific one. I
have a few million (independent) short regressions that I would like
to run (each reg has about 60 observations, though they can have
missing observations [yikes]). So, I would like to be running as many
`lm` and `coef(lm)` in parallel as possible. my hardware is Mac, with
nice GPUs and integrated memory --- and so far completely useless to
me. `mclapply` is obviously very useful, but I want more, more, more
cores.

is there a recommended plug-in library to speed up just `lm` by also
using the GPU cores?

______________________________________________
R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide https://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.


	[[alternative HTML version deleted]]

>>>>> Andrew Robinson via R-help 
>>>>>     on Thu, 14 Nov 2024 12:45:44 +0000 writes:
    > Not a direct answer but you may find lm.fit worth
    > experimenting with.  

Yes, lm.fit() is already faster, and
    .lm.fit() {added to base R by me, when a similar question
    was asked years ago ...}
    is even an order of magnitude faster  in some cases.

See ?lm.fit
and notably

example(lm.fit)

which uses pkg microbenchmark for timing and  after which

   png("lmfit-ex.png")
   boxplot(mb, notch=TRUE)
   dev.off()

produces the attached nice image.

    > Also try the high-performance computing task view on CRAN

    > Cheers,
    > Andrew

    > --
    > Andrew Robinson Chief Executive Officer, CEBRA and
    > Professor of Biosecurity, School/s of BioSciences and
    > Mathematics & Statistics University of Melbourne, VIC 3010
    > Australia Tel: (+61) 0403 138 955 Email:
    > apro at unimelb.edu.au Website:
    > https://researchers.ms.unimelb.edu.au/~apro at unimelb/

    > I acknowledge the Traditional Owners of the land I
    > inhabit, and pay my respects to their Elders.  On 14 Nov
    > 2024 at 1:13?PM +0100, Ivo Welch <ivo.welch at gmail.com>,
    > wrote: External email: Please exercise caution

    > I have found more general questions, but I have a specific
    > one. I have a few million (independent) short regressions
    > that I would like to run (each reg has about 60
    > observations, though they can have missing observations
    > [yikes]). So, I would like to be running as many `lm` and
    > `coef(lm)` in parallel as possible. my hardware is Mac,
    > with nice GPUs and integrated memory --- and so far
    > completely useless to me. `mclapply` is obviously very
    > useful, but I want more, more, more cores.

    > is there a recommended plug-in library to speed up just
    > `lm` by also using the GPU cores?



-------------- next part --------------
A non-text attachment was scrubbed...
Name: lmfit-ex.png
Type: image/png
Size: 7605 bytes
Desc: not available
URL:
<https://stat.ethz.ch/pipermail/r-help/attachments/20241115/9ee1c007/attachment.png>