[Numpy-discussion] fortran vs numpy on mac/linux - gcc performance?

[Robin]

Hi,

I have been looking at moving some of my bottleneck functions to
fortran with f2py. To get started I tried some simple things, and was
surprised they performend so much better than the number builtins -
which I assumed would be c and would be quite fast.

On my Macbook pro laptop (Intel core 2 duo) I got the following
results. Numpy is built with xcode gcc 4.0.1 and gfortran is 4.2.3 -
fortran code for shuffle and bincount below:
In [1]: x = np.random.random_integers(0,1023,1000000).astype(int)
In [2]: import ftest
In [3]: timeit np.bincount(x)
100 loops, best of 3: 3.97 ms per loop
In [4]: timeit ftest.bincount(x,1024)
1000 loops, best of 3: 1.15 ms per loop
In [5]: timeit np.random.shuffle(x)
1 loops, best of 3: 605 ms per loop
In [6]: timeit ftest.shuffle(x)
10 loops, best of 3: 139 ms per loop

So fortran was about 4 times faster for these loops - similarly faster
than cython as well. So I was really happy as these are two of my
biggest bottlenecks, but when I moved a linux workstation I got
different results. Here with gcc/gfortran 4.3.3 :
In [3]: x = np.random.random_integers(0,1023,1000000).astype(int)
In [4]: timeit np.bincount(x)
100 loops, best of 3: 8.18 ms per loop
In [5]: timeit ftest.bincount(x,1024)
100 loops, best of 3: 8.25 ms per loop
In [6]:
In [7]: timeit np.random.shuffle(x)
1 loops, best of 3: 379 ms per loop
In [8]: timeit ftest.shuffle(x)
10 loops, best of 3: 172 ms per loop

So shuffle is a bit faster, but bincount is now the same as fortran.
The only thing I can think is that it is due to much better
performance of the more recent c compiler. I think this would also
explain why f2py extension was performing so much better than cython
on the mac.

So my question is -  is there a way to build numpy with a more recent
compiler on leopard? (I guess I could upgrade to snow leopard now) -
Could I make the numpy install use gcc-4.2 from xcode or would it
break stuff? Could I use gcc 4.3.3 from macports? It would be great to
get a 4x speed up on all numpy c loops! (already just these two
functions I use a lot would make a big difference).

Cheers

Robin