2011/10/11 Skipper Seabold <jsseab...@gmail.com> > On Tue, Oct 11, 2011 at 11:57 AM, Christoph Groth <c...@falma.de> wrote: > > Pauli Virtanen <p...@iki.fi> writes: > > > >>> Thank you for your suggestion. It doesn't help me however, because > >>> the algorithm I'm _really_ trying to speed up cannot be vectorized > >>> with numpy in the way you vectorized my toy example. > >>> > >>> Any other ideas? > >> > >> Reformulate the problem so that it can be vectorized. Without knowing > >> more about the actual algorithm you are trying to implement, it's not > >> easy to give more detailed help. > > > > My question was about ways to achieve a speedup without modifying the > > algorithm. I was hoping that there is some numpy-like library for > > python which for small arrays achieves a performance at least on par > > with the implementation using tuples. This should be possible > > technically. > > So it's the dot function being called repeatedly on smallish arrays > that's the bottleneck? I've run into this as well. See this thread > [1]. You might gain some speed if you drop it down into Cython, some > examples in that thread. If you're still up against it, you can try > the C code that Fernando posted for fast matrix multiplication (I > haven't yet), or you might be able to do well to use tokyo from Cython > since Wes' has fixed it up [2]. > > I'd be very interested to hear if you achieve a great speed-up with > cython+tokyo. > > Cheers, > > Skipper > > [1] > http://mail.scipy.org/pipermail/scipy-user/2010-December/thread.html#27791 > [2] https://github.com/wesm/tokyo > > Another idea would be to use Theano ( http://deeplearning.net/software/theano/). It's a bit overkill though and you would need to express most of your algorithm in a symbolic way to be able to take advantage of it. You would then be able to write your own C code to do the array operations that are too slow when relying on numpy. If you are interested in pursuing this direction though, let me know and I can give you a few pointers.
-=- Olivier
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