Speeding up numpy.dot

Question

I\'ve got a numpy script that spends about 50% of its runtime in the following code:

s = numpy.dot(v1, v1)

where

v1 = v[1

Answer 1

Perhaps the culprit is copying of the arrays passed to dot.

As Sven said, the dot product relies on BLAS operations. These operations require arrays stored in contiguous C order. If both arrays passed to dot are in C_CONTIGUOUS, you ought to see better performance.

Of course, if your two arrays passed to dot are indeed 1D (8,) then you should see both the C_CONTIGUOUS AND F_CONTIGUOUS flags set to True; but if they are (1, 8), then you can see mixed order.

>>> w = NP.random.randint(0, 10, 100).reshape(100, 1)
>>> w.flags
   C_CONTIGUOUS : True
   F_CONTIGUOUS : False
   OWNDATA : False
   WRITEABLE : True
   ALIGNED : True
   UPDATEIFCOPY : False

An alternative: use _GEMM from BLAS, which is exposed through the module, scipy.linalg.fblas. (The two arrays, A and B, are obviously in Fortran order because fblas is used.)

from scipy.linalg import fblas as FB
X = FB.dgemm(alpha=1., a=A, b=B, trans_b=True)