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 [EMAIL PROTECTED] .../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 > > > > ------------------------------------------------------------------------ > > _______________________________________________ > Numpy-discussion mailing list > [email protected] > http://projects.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ Numpy-discussion mailing list [email protected] http://projects.scipy.org/mailman/listinfo/numpy-discussion
