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 <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:
* g++/clang new enough to be compatible with nanobind (specifically, full support of C++17 is needed)
* `numpy <https://numpy.org>`__, and
* an OpenCL implementation. (See this `howto <https://wiki.tiker.net/OpenCLHowTo>`__
for how to get one.)
Links
-----
* `Documentation <https://documen.tician.de/pyopencl>`__
(read how things work)
* `Python package index <https://pypi.python.org/pypi/pyopencl>`__
(download releases, including binary wheels for Linux, macOS, Windows)
* `Conda Forge <https://anaconda.org/conda-forge/pyopencl>`__
(download binary packages for Linux, macOS, Windows)
* `Github <https://github.com/inducer/pyopencl>`__
(get latest source code, file bugs)
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