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.. highlight:: python
.. _numpy-pythran:
**************************
Pythran as a Numpy backend
**************************
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.
Usage example with setuptools
-----------------------------
You first need to install Pythran. See its `documentation
<https://pythran.readthedocs.io/>`_ 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 setuptools:
.. code::
from setuptools import setup
from Cython.Build import cythonize
setup(
name = "My hello app",
ext_modules = cythonize('hello_pythran.pyx')
)
Then, with the following header in ``hello_pythran.pyx``:
.. code::
# 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
<https://pythran.readthedocs.io/en/latest/MANUAL.html#customizing-your-pythranrc>`_ for
more information.
.. _Pythran: https://github.com/serge-sans-paille/pythran
.. _Boost.SIMD: https://github.com/NumScale/boost.simd
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