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All Projects → inducer → Pyopencl

inducer / Pyopencl

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OpenCL integration for Python, plus shiny features

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python
139335 projects - #7 most used programming language

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performanceopenglgpucudanvidiaopenclscientific-computingparallel-computingarrayamdsortingshared-memorymultidimensional-arrays

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PyOpenCL: Pythonic Access to OpenCL, with Arrays and Algorithms

.. image:: https://gitlab.tiker.net/inducer/pyopencl/badges/main/pipeline.svg :alt: Gitlab Build Status :target: https://gitlab.tiker.net/inducer/pyopencl/commits/main .. image:: https://github.com/inducer/pyopencl/workflows/CI/badge.svg?branch=main&event=push :alt: Github Build Status :target: https://github.com/inducer/pyopencl/actions?query=branch%3Amain+workflow%3ACI+event%3Apush .. image:: https://badge.fury.io/py/pyopencl.png :alt: Python Package Index Release Page :target: https://pypi.org/project/pyopencl/

PyOpenCL lets you access GPUs and other massively parallel compute devices from Python. It tries to offer computing goodness in the spirit of its sister project PyCUDA <https://mathema.tician.de/software/pycuda>_:

  • Object cleanup tied to lifetime of objects. This idiom, often called RAII <https://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>_ in C++, makes it much easier to write correct, leak- and crash-free code.

  • Completeness. PyOpenCL puts the full power of OpenCL's API at your disposal, if you wish. Every obscure get_info() query and all CL calls are accessible.

  • Automatic Error Checking. All CL errors are automatically translated into Python exceptions.

  • Speed. PyOpenCL's base layer is written in C++, so all the niceties above are virtually free.

  • Helpful and complete Documentation <https://documen.tician.de/pyopencl>__ as well as a Wiki <https://wiki.tiker.net/PyOpenCL>_.

  • Liberal license. PyOpenCL is open-source under the MIT license <https://en.wikipedia.org/wiki/MIT_License>_ and free for commercial, academic, and private use.

  • Broad support. PyOpenCL was tested and works with Apple's, AMD's, and Nvidia's CL implementations.

Simple 4-step install instructions <https://documen.tician.de/pyopencl/misc.html#installation>_ using Conda on Linux and macOS (that also install a working OpenCL implementation!) can be found in the documentation <https://documen.tician.de/pyopencl/>__.

What you'll need if you do not want to use the convenient instructions above and instead build from source:

  • gcc/g++ new enough to be compatible with pybind11 (see their FAQ <https://pybind11.readthedocs.io/en/stable/faq.html>_)
  • numpy <https://numpy.org>_, and
  • an OpenCL implementation. (See this howto <https://wiki.tiker.net/OpenCLHowTo>_ for how to get one.)

Places on the web related to PyOpenCL:

  • Python package index <https://pypi.python.org/pypi/pyopencl>_ (download releases)

  • Documentation <https://documen.tician.de/pyopencl>__ (read how things work)

  • Conda Forge <https://anaconda.org/conda-forge/pyopencl>_ (download binary packages for Linux, macOS, Windows)

  • C. Gohlke's Windows binaries <https://www.lfd.uci.edu/~gohlke/pythonlibs/#pyopencl>_ (download Windows binaries)

  • Github <https://github.com/inducer/pyopencl>_ (get latest source code, file bugs)

  • Wiki <https://wiki.tiker.net/PyOpenCL>_ (read installation tips, get examples, read FAQ)

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