Hi Valerio,
The dot function is correctly implemented for "a * b" with dim(a) <= 2
and dim(b) <= 2 in the dot branch
(https://github.com/serge-sans-paille/pythran/pull/417)
It will not be integrated in the next Pythran release because it
requires an up to date nt2 version and Pythran is based on the release
version but it will be integrated in the master branch right after the
release.
I hope you succeed to use Pythran,
cheers,
Pierrick
On 26/03/2015 19:41, serge guelton wrote:
On Thu, Mar 26, 2015 at 04:13:17PM +0000, Valerio De Carolis wrote:
On 25/03/15 18:31, Pierrick Brunet wrote:Yes, that's the on-going direction. Pierrick explored this today.
Hi Valerio,
It is great to see Pythran used that way and thanks for these kind of
feed back.
About numpy.dot, only the vec * vec implementation exists for now but it
is planned to add this with the numpy.linalg package.
To get around this problem, we will be really happy if you want to add
this implementation in Pythran :-)
Otherwise, I may have a look at this tomorrow for, at least, a "slow"
implementation for compatibility.
If you want to test it now. I suggest you to implement this function in
you Python module (using Python) and Pythran will convert it for you.
Cheers,
Pierrick
Hi Pierrick,
yes I gave it a quick try with a double for approach:
#pythran export mat_vec_mult(float[][], float[])This is working well in Pythran and even for a small matrix if the code is
def mat_vec_mult(A, b):
c = np.zeros_like(b)
for i in xrange(A.shape[0]):
for j in xrange(A.shape[1]):
c[i] += A[i,j] * b[j]
return c
using the non-optimized version. I haven't benchmarked it too much though.
I'm not sure what is the best approach for the implementation of such
feature in Pythran. Numpy internally defaults to BLAS's GEMV and GEMM style
functions. So I think the use of BLAS primitives is the natural translation
from numpy-ish code to the lower-level C++.
