[Numpy-discussion] numpy.dot and ACML

Toon Knapen toon.knapen@fft...
Thu Mar 1 01:04:08 CST 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@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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