On Aug 18, 2009, at 11:46 PM, Aaron S. Meurer wrote:
> Hi. I am a developer for the project SymPy [0], which is a computer
> algebra system written in Python. We have been considering moving
> part or all of the code base into Cython for speed reasons. Ondrej
> Certik, the project's leader, recently gave a tutorial at SciPy09
> (there should be a video up soon), which included a bit about
> increasing the speed of an operation by about 20x by using the
> cython.locals() decorator to convert some of the variables into cython
> ints. You can pull from his github account [1]. If you run:
>
>>>> from sympy import *
>>>> d = divisors(5*10**7)
>
> The function will run about 20x to 30x faster, as I mentioned. But if
> you run the ntheory test, which uses divisors, using
>
> $./bin/test sympy/ntheory
>
> it runs slower or at least as slow as without cython. We figured out
> that the reason is that when the multiple imports in the tests are
> called, they run about 2x slower with cython, because it calls the
> decorator each time.
>
> You can see this by running
>
> In [1]: %timeit from sympy import *
>
> with and without cython in IPython.
>
> Is there any way around this? This could be a show stopper for us,
> because the increased import times levels out or makes worse the total
> times for things. This is not exclusive to the test suite. It comes
> from multiple imports in each file.
>
> We know that we can use .pxd files, but that would require to handle
> both .pxd files and .py files instead of maintaining one file with
> everything.
Eventually I want to get type inference, which could help here.
However, I'm not following how decorating these functions makes
everything slower. Sure, it's a bit slower on the first import, but
the decorator is no re-called on each import. Clearly I'm missing
something here, could you explain why the decorator is being
repeatedly called?
In the worst case, maintaining separate .pxd files seems a small
price to pay to get a 20-30x speedup. Also, if you want to have cdef
classes, especially across various modules, you'll have to do that
anyways right now. (Patches for fixing this, e.g. via class
decorators, would be highly appreciated).
- Robert
------------------------- foo.py -----------------------
def decorate(f):
print "here"
return f
@decorate
def f(x):
return x
----------------------------------------------------------
sage: from foo import *
here
sage: from foo import *
sage: from foo import *
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