Created
October 16, 2012 18:37
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hn wes benchmark followup
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import numpy as np | |
cimport numpy as np | |
ctypedef np.float64_t float64_t | |
def inner(np.ndarray[float64_t] x, np.ndarray[float64_t] y): | |
cdef Py_ssize_t i, n = len(x) | |
cdef float64_t result = 0 | |
for i in range(n): | |
result += x[i] * y[i] | |
return result |
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from distutils.core import setup | |
from distutils.extension import Extension | |
from Cython.Distutils import build_ext | |
import numpy | |
setup( | |
cmdclass = {'build_ext': build_ext}, | |
ext_modules = [Extension("inner", ["inner.pyx"],include_dirs=[numpy.get_include()])] | |
) |
Aaaand finally array operations in Julia (via sum(x.*y)
), which is really the better comparison to the numpy example, took 91.3 ms.
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here's the timing of
inner()
in Julia:inner took 37.441840171813965 ms
See Wes's blog post for details on this stuff.