[Numpy-discussion] numpy.dot and ACML

Yves Frederix yves.frederix@gmail....
Mon Feb 19 11:11:20 CST 2007


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
-------------- next part --------------
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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