[Numpy-discussion] numpy.dot and ACML
Toon Knapen
toon.knapen at fft.be
Thu Mar 1 02:04:08 EST 2007
Hi,
I am also looking to verify the vendor-libs being used.
What does numpy.__config__.show() tell you ?
toon
Yves Frederix wrote:
> Hi all,
>
> I have managed to compile numpy using pathscale and ACML on a 64 bit AMD
> system. Now I wanted to verify that numpy.dot indeed uses the ACML
> libs. The example for dot()
> (http://www.scipy.org/Numpy_Example_List?highlight=%28example%29#head-c7a573f030ff7cbaea62baf219599b3976136bac) suggest a way of doing this:
>
> 1 u0050015 at lo-03-02 .../core $ python -c "import numpy; print id(numpy.dot)==id(numpy.core.multiarray.dot);"
> True
>
> This indicates that I am not using the acml libraries.
>
> When running a benchmark (see attach) and comparing to a non-ACML
> installation though, the strange thing is that there is a clear
> speed difference, suggesting again that the acml libraries are indeed
> used.
>
> Because this is not all that clear to me, I was wondering whether there
> exists an alternative way of verifying what libraries are used.
>
> Many thanks,
> YVES
>
>
> ------------------------------------------------------------------------
>
> ACML:
>
> dim x.T*y x*y.T A*x A*B A.T*x
> -----------------------------------------------------------------
> 5000 0.002492 0.002417 0.002412 0.002399 0.002416
> 50000 0.020074 0.020024 0.020004 0.020003 0.020024
> 100000 0.092777 0.093690 0.100220 0.093787 0.094250
> 200000 0.184933 0.198623 0.196120 0.197089 0.197273
> 300000 0.276583 0.279177 0.280898 0.284016 0.276204
> 500000 0.476340 0.481987 0.471875 0.480868 0.481501
> 1000000.0 0.892623 0.895500 0.915173 0.894815 0.922501
> 5000000.0 4.450555 4.465748 4.467870 4.468188 4.469083
>
> No ACML:
>
> dim x.T*y x*y.T A*x A*B A.T*x
> -----------------------------------------------------------------
> 5000 0.002523 0.002428 0.002410 0.002430 0.002419
> 50000 0.024756 0.061520 0.036575 0.036399 0.036450
> 100000 0.338576 0.353074 0.169472 0.302087 0.334633
> 200000 0.670803 0.735732 0.538166 0.649335 0.744496
> 300000 1.004381 1.269259 0.482542 2.194308 0.611997
> 500000 1.110656 1.504701 1.571736 1.656021 1.491146
> 1000000.0 2.182746 2.234478 2.254645 2.439508 2.537558
> 5000000.0 10.878910 16.578266 8.265109 8.905976 17.124400
>
>
>
> ------------------------------------------------------------------------
>
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