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:]

and v is a 4000-element 1D ndarray of float64 stored in contiguous memory (v.strides is (8,)).

Any suggestions for speeding this up?

edit This is on Intel hardware. Here is the output of my numpy.show_config():

atlas_threads_info:
    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
    library_dirs = ['/usr/local/atlas-3.9.16/lib']
    language = f77
    include_dirs = ['/usr/local/atlas-3.9.16/include']

blas_opt_info:
    libraries = ['ptf77blas', 'ptcblas', 'atlas']
    library_dirs = ['/usr/local/atlas-3.9.16/lib']
    define_macros = [('ATLAS_INFO', '"\\"3.9.16\\""')]
    language = c
    include_dirs = ['/usr/local/atlas-3.9.16/include']

atlas_blas_threads_info:
    libraries = ['ptf77blas', 'ptcblas', 'atlas']
    library_dirs = ['/usr/local/atlas-3.9.16/lib']
    language = c
    include_dirs = ['/usr/local/atlas-3.9.16/include']

lapack_opt_info:
    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
    library_dirs = ['/usr/local/atlas-3.9.16/lib']
    define_macros = [('ATLAS_INFO', '"\\"3.9.16\\""')]
    language = f77
    include_dirs = ['/usr/local/atlas-3.9.16/include']

lapack_mkl_info:
  NOT AVAILABLE

blas_mkl_info:
  NOT AVAILABLE

mkl_info:
  NOT AVAILABLE

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)