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https://github.com/inducer/pyopencl
OpenCL integration for Python, plus shiny features
https://github.com/inducer/pyopencl
amd array cuda gpu heterogeneous-parallel-programming multidimensional-arrays nvidia opencl opengl parallel-algorithm parallel-computing performance prefix-sum pyopencl python reduction scientific-computing shared-memory sorting
Last synced: 25 days ago
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OpenCL integration for Python, plus shiny features
- Host: GitHub
- URL: https://github.com/inducer/pyopencl
- Owner: inducer
- License: other
- Created: 2011-04-06T02:51:33.000Z (over 13 years ago)
- Default Branch: main
- Last Pushed: 2024-08-30T16:16:32.000Z (2 months ago)
- Last Synced: 2024-10-01T11:42:53.238Z (about 1 month ago)
- Topics: amd, array, cuda, gpu, heterogeneous-parallel-programming, multidimensional-arrays, nvidia, opencl, opengl, parallel-algorithm, parallel-computing, performance, prefix-sum, pyopencl, python, reduction, scientific-computing, shared-memory, sorting
- Language: Python
- Homepage: http://mathema.tician.de/software/pyopencl
- Size: 5.39 MB
- Stars: 1,057
- Watchers: 50
- Forks: 241
- Open Issues: 76
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
- Citation: CITATION.cff
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README
PyOpenCL: Pythonic Access to OpenCL, with Arrays and Algorithms
===============================================================
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:alt: Python Package Index Release Page
:target: https://pypi.org/project/pyopencl/
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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.)
Links
-----
* `Documentation `__
(read how things work)
* `Python package index `__
(download releases, including binary wheels for Linux, macOS, Windows)
* `Conda Forge `__
(download binary packages for Linux, macOS, Windows)
* `Github `__
(get latest source code, file bugs)