Hi,

following the excellent advice of V. Armando Sole, I have finally succeeded in calling the blas routines shipped with scipy from cython.

I am doing this to avoid shipping an extra blas library for some project of mine that uses scipy but has some things coded in cython for extra speed.

So far I managed getting things working on Linux. Here is what I do:

The following code snippet gives me the dgemv pointer (which is a pointer to a fortran function, even if it comes from scipy.linalg.blas.cblas, weird).

from cpython cimport PyCObject_AsVoidPtr import scipy as sp __import__('scipy.linalg.blas')

ctypedef void (*dgemv_ptr) (char *trans, int *m, int *n,\ double *alpha, double *a, int *lda, double *x,\ int *incx,\ double *beta, double *y, int *incy) cdef dgemv_ptr dgemv=<dgemv_ptr>PyCObject_AsVoidPtr(\ sp.linalg.blas.cblas.dgemv._cpointer)

Then, in a tight loop, I can call dgemv by first defining the constants and then calling dgemv inside the loop

cdef int one=1 cdef double onedot = 1.0 cdef double zerodot = 0.0 cdef char trans = 'N' for i in xrange(N): dgemv(&trans, &nq, &order,\ &onedot, <double *>np.PyArray_DATA(C), &order, \ <double*>np.PyArray_DATA(c_x0), &one, \ &zerodot, <double*>np.PyArray_DATA(y0), &one)

It works, but it is many many times slower than linking to the cblas that is available on the same system. Specifically, I have about 8 calls to blas in my tight loop, 4 of them are to dgemv and the others are to dcopy. Changing a single dgemv call from the system cblas to the blas function returned by scipy.linalg.blas.cblas.dgemv._cpointer makes the execution time of a test case jump from about 0.7 s to 1.25 on my system.

Any clue about why is this happening?

In the end, on linux, scipy dynamically link to atlas exactly as I link to atlas when I use the cblas functions.