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Latest commit 7c2f3ba Jul 11, 2018

README.rst

PyOpenCL: Pythonic Access to OpenCL, with Arrays and Algorithms

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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:

  • Object cleanup tied to lifetime of objects. This idiom, often called RAII 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 as well as a Wiki.
  • Liberal license. PyOpenCL is open-source under the 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.

What you'll need:

  • gcc/g++ at or newer than version 4.8.2 and binutils at or newer than 2.23.52.0.1-10 (CentOS version number). On Windows, use the mingwpy compilers.
  • numpy, and
  • an OpenCL implementation. (See this howto for how to get one.)

Places on the web related to PyOpenCL: