Python wrapper for OpenCL

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  • License: MIT License
  • Author: Andreas Kloeckner
  • Requires: Python ~=3.8
  • Provides-Extra: oclgrind, pocl, test
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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.

Simple 4-step install instructions using Conda on Linux and macOS (that also install a working OpenCL implementation!) can be found in the documentation.

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

  • g++/clang new enough to be compatible with nanobind (specifically, full support of C++17 is needed)

  • numpy, and

  • an OpenCL implementation. (See this howto for how to get one.)

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