No, I'm just using the 0.3.4 release build from the website. I don't have a personal MKL license. The python build is from Continuum via their Anaconda distribution with the accelerate add-on (free for academics).
Josh On Jan 8, 2015, at 4:21 PM, Jiahao Chen wrote: > Thanks for the update. Are you also building Julia with MKL also then? > > On Thu Jan 08 2015 at 4:19:48 PM Joshua Adelman <joshua.adel...@gmail.com> > wrote: > Hi Jiahao, > > Just a small note - based on your comments in the thread you mentioned, I > ended up changing my test to just multiply ones to avoid over/underflow. > Those are the results that are now in that notebook, so that shouldn't be an > issue in the plotted timings. On the python side, I'm using Numpy 1.9 via the > Anaconda distribution built against MKL. > > Josh > > > On Jan 8, 2015, at 4:15 PM, Jiahao Chen wrote: > > > As Stefan wrote, all you are really doing with larger matrix tests is > > testing the speed of the different BLAS implementations being used by your > > distributions of Julia and NumPy. > > > > As I wrote in the other thread > > > > https://groups.google.com/d/msg/julia-users/Q96aPufg4S8/IBU9hW0xvWYJ > > > > the Vandermonde matrix generation is significantly faster for me in Julia > > than in Python (numpy using reference BLAS). > > > > Furthermore Vandermonde is not a good test with larger matrix sizes since > > you are basically testing the speed of multiplying things by infinity, > > which may not be representative of typical computations as it may incur > > overhead from handling overflows. > > > > For n=10000 I get the following results: > > > > Macbook Julia (Core(TM) i5-4258U) > > +/-1 matrix: 1.22s > > [1:n] matrix: 1.21s #mostly overflow > > rand matrix: 2.95s #mostly underflow > > > > Macbook NumPy > > +/-1 matrix: 3.96s > > [1:n] matrix: 5.04s #mostly overflow > > rand matrix: 5.46s #mostly underflow > > > > Linux Julia (Xeon(R) E7- 8850) > > +/-1 matrix: 2.18s > > [1:n] matrix: 1.89s #mostly overflow > > rand matrix: 4.36s #mostly underflow > > > > Linux NumPy > > +/-1 matrix: 9.38s > > [1:n] matrix: 10.64s #mostly overflow * > > rand matrix: 32.30s #mostly underflow > > > > * emits warnings: > > /usr/lib/python2.7/dist-packages/numpy/lib/twodim_base.py:515: > > RuntimeWarning: overflow encountered in power > > X[:, i] = x**(N - i - 1) > > /usr/lib/python2.7/dist-packages/numpy/lib/twodim_base.py:515: > > RuntimeWarning: invalid value encountered in power > > X[:, i] = x**(N - i - 1) >
