Pythran 作为 Numpy 后端 ¶

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.

distutils 的用法范例 ¶

You first need to install Pythran. See its 文档编制 了解更多信息。

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 hello_pythran.pyx :

# 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 了解更多信息。

内容表

• Pythran 作为 Numpy 后端
  • distutils 的用法范例

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