Hi both
Just to corroborate Simon's numbers, my M1 Max (MBP 14" with 64GB ram) gets the
following for vecLib:
> source("CuriousResult.R")
matrix multiplication 2.07 0.527 1.571
tcrossprod 2.328 0.55 1.688
transposition and reuse 5.898 0.844 6.743
elementwise after reshape 4.894 0.523 5.421
columnwise sapply 4.767 1.445 6.212
for loop over columns 4.489 1.908 6.398
Which compares to the following for R reference BLAS:
> source("CuriousResult.R")
matrix multiplication 37.511 0.762 38.282
tcrossprod 37.519 0.737 38.254
transposition and reuse 5.858 0.909 6.767
elementwise after reshape 4.841 0.471 5.311
columnwise sapply 4.778 1.646 6.432
for loop over columns 4.485 1.948 6.434
Both on the arm64 version of R.
As an aside - I am extremely happy with this machine (albeit after only a few
days of ownership) and would happily recommend it over the 2017 MBP that it has
replaced.
Best,
Matt
On 10/02/2022, 04:22, "R-SIG-Mac on behalf of Simon Urbanek"
<r-sig-mac-boun...@r-project.org on behalf of simon.urba...@r-project.org>
wrote:
Joe,
are you sure you are using the native arm64 version of R with the
Accelerate bindings (formerly vecLib)? The one really cool thing about M1 is
that it is very much faster than any Intel Macs (in some examples more than
10x) so what you report doesn't look right. With your script on a
first-generation M1 mac mini (so the slower one) I get:
> source("CuriousResult.R")
matrix multiplication 2.084 0.401 2.488
tcrossprod 2.284 0.396 2.68
transposition and reuse 5.771 0.786 6.557
elementwise after reshape 4.974 0.586 5.56
columnwise sapply 5.675 1.451 7.127
for loop over columns 4.565 1.633 6.199
so I would suspect that you may be running Intel R emulated and without
vecLib?
Cheers,
Simon
> On Feb 10, 2022, at 12:44 PM, Joseph Kunkel <j...@bio.umass.edu> wrote:
>
> Carl, I would not buy a Mac M1Mag with 64 GB of memory. I just bought
one and it is not as good as my 2013 MacBook Pro with 32 GB and 4 cores.
>
> With my 2013 MacBook Pro I could configure R to do is matrix
multiplication with the fast vecLib library and it would use core doubling so
that I was using up to 800% of my resources to do my big math.
>
> With my 2013 I could run the following benchmark and it would execute in
1/4 the time.
>
>>
source("https://eur02.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.bio.umass.edu%2Fbiology%2Fkunkel%2Fpub%2FR%2FCuriousResult.R&data=04%7C01%7Cmd%40sund.ku.dk%7C0e47fdeeaf4849cfff8b08d9ec446514%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C637800601372950867%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=pgiqgJBJ5KY%2BTSIm43aZQWnyaMEwTvDua3y%2Fj6ThGt4%3D&reserved=0";)
>
> matrix multiplication 21.248 1.608 5.891
> tcrossprod 20.346 1.454 5.39
> transposition and reuse 15.881 2.887 18.657
> elementwise after reshape 9.637 2.144 11.717
> columnwise sapply 13.447 11.982 25.241
> for loop over columns 12.435 10.14 22.44
>
> Now with R on the MacBook Pro with M1Max it takes longer to do the base
multiplication and there is no accelerated option although it does the simpler
non sectoral approaches faster …
>
>>
source("https://eur02.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.bio.umass.edu%2Fbiology%2Fkunkel%2Fpub%2FR%2FCuriousResult.R&data=04%7C01%7Cmd%40sund.ku.dk%7C0e47fdeeaf4849cfff8b08d9ec446514%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C637800601372950867%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=pgiqgJBJ5KY%2BTSIm43aZQWnyaMEwTvDua3y%2Fj6ThGt4%3D&reserved=0";)
> matrix multiplication 40.476 0.127 40.322
> tcrossprod 40.347 0.056 40.282
> transposition and reuse 6.776 0.005 6.78
> elementwise after reshape 7.849 0.016 7.868
> columnwise sapply 4.481 0.114 4.592
> for loop over columns 4.282 0.271 4.55
>
> I am searching for why.
>
> Joe Kunkel
> -·. .· ·. .><((((º>·. .· ·. .><((((º>·. .· ·. .><((((º> .··.· >=-
=º}}}}}><
> Joseph G. Kunkel, Emeritus Professor
> Biology Department
> UMass Amherst
> Amherst MA 01003
> j...@bio.umass.edu
>
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