Using the flag
--np-pythran, it is possible to use the Pythran numpy
implementation for numpy related operations. One advantage to use this backend
is that the Pythran implementation uses C++ expression templates to save memory
transfers and can benefit from SIMD instructions of modern CPU.
This can lead to really interesting speedup in some cases, going from 2 up to 16, depending on the targeted CPU architecture and the original algorithm.
Please note that this feature is experimental.
You first need to install Pythran. See its documentation for more information.
Then, simply add a
cython: np_pythran=True directive at the top of the
Python files that needs to be compiled using Pythran numpy support.
Here is an example of a simple
setup.py file using distutils:
from distutils.core import setup from Cython.Build import cythonize setup( name = "My hello app", ext_modules = cythonize('hello_pythran.pyx') )
Then, with the following header in
# cython: np_pythran=True
hello_pythran.pyx will be compiled using Pythran numpy support.
Please note that Pythran can further be tweaked by adding settings in the
$HOME/.pythranrc file. For instance, this can be used to enable Boost.SIMD support.
See the Pythran user manual for