[R-SIG-Mac] https://mac.r-project.org/benchmarks/

[Ken Beath]

I ran some tests using my packages and a bootstrapped linear model, and the results were that my M1 MacBook Air was faster than my iMac 8-core 2020, and that included using 4 parallel cores on the M1 versus 8 on the iMac. 

Ken

> On 1 Nov 2021, at 8:11 am, Simon Urbanek <simon.urbanek using R-project.org> wrote:
> 
> 
> Tim,
> 
> that is a great idea, those test are really old. Just for the fun of it I have run the tests on my old iMac, but with R 4.1.2 and they still work.
> It's nice to see the huge speed improvements in loops and similar (see below - recall the original tests were scaled to be around 1).
> 
> I have added the page to the repo
> https://github.com/R-macos/R-mac-dev
> so I'd be happy to review PRs, but I'll probably want to re-do it first so it is better organized for comparisons as we have to also accommodate M1 etc.
> 
> Cheers,
> Simon
> 
> ---
> iMac14,2 3.2Ghz i5, macOS 10.4.6, R 4.1.2 vecib/Accelerate BLAS
> 
> 
>   R Benchmark 2.5
>   ===============
> Number of times each test is run__________________________:  3
> 
>   I. Matrix calculation
>   ---------------------
> Creation, transp., deformation of a 2500x2500 matrix (sec):  0.829666666666667 
> 2400x2400 normal distributed random matrix ^1000____ (sec):  0.155333333333334 
> Sorting of 7,000,000 random values__________________ (sec):  0.638333333333334 
> 2800x2800 cross-product matrix (b = a' * a)_________ (sec):  0.242000000000001 
> Linear regr. over a 3000x3000 matrix (c = a \ b')___ (sec):  0.170999999999999 
>                      --------------------------------------------
>                 Trimmed geom. mean (2 extremes eliminated):  0.29781941072597 
> 
>   II. Matrix functions
>   --------------------
> FFT over 2,400,000 random values____________________ (sec):  0.331333333333333 
> Eigenvalues of a 640x640 random matrix______________ (sec):  0.347000000000001 
> Determinant of a 2500x2500 random matrix____________ (sec):  0.207000000000001 
> Cholesky decomposition of a 3000x3000 matrix________ (sec):  0.254333333333334 
> Inverse of a 1600x1600 random matrix________________ (sec):  0.345666666666663 
>                      --------------------------------------------
>                Trimmed geom. mean (2 extremes eliminated):  0.307686639256803 
> 
>   III. Programmation
>   ------------------
> 3,500,000 Fibonacci numbers calculation (vector calc)(sec):  0.245 
> Creation of a 3000x3000 Hilbert matrix (matrix calc) (sec):  0.289666666666669 
> Grand common divisors of 400,000 pairs (recursion)__ (sec):  0.259333333333331 
> Creation of a 500x500 Toeplitz matrix (loops)_______ (sec):  0.0400000000000015 
> Escoufier's method on a 45x45 matrix (mixed)________ (sec):  0.263000000000005 
>                      --------------------------------------------
>                Trimmed geom. mean (2 extremes eliminated):  0.255658395143118 
> 
> 
> Total time for all 15 tests_________________________ (sec):  4.61866666666667 
> Overall mean (sum of I, II and III trimmed means/3)_ (sec):  0.286136920519432 
>                      --- End of test ---
> 
> 
> 
>> On Nov 1, 2021, at 2:48 AM, Tim Bates <timothy.c.bates using gmail.com> wrote:
>> 
>> I wonder if this (2008/R 2.7) page could be updated with some current benchmark runs?
>> 
>> Especially current Intel server chips, i9, and M1/+ 
>> 
>> I'm guessing if Simon could help upload the resulting updated page, people here could contribute bench mark runs on different hardware.
>> 
>> 
>> Also be interesting to see different blas results.
>> 
>> I wonder if either intel or arm chip "neural" cores (dot product engines?) or multi-core and GPU are being used in current R builds?
>> 
>> tim
>> 
>> 
>> 
>> 
>> 
>> 
>> 
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