https://github.com/inducer/pycuda

CUDA integration for Python, plus shiny features
https://github.com/inducer/pycuda

array cuda gpu gpu-computing multidimensional-arrays pycuda python scientific-computing

Last synced: 10 days ago
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CUDA integration for Python, plus shiny features

• Host: GitHub
• URL: https://github.com/inducer/pycuda
• Owner: inducer
• License: other
• Created: 2011-04-06T02:53:31.000Z (over 13 years ago)
• Default Branch: main
• Last Pushed: 2024-10-17T01:51:01.000Z (22 days ago)
• Last Synced: 2024-10-19T03:44:00.036Z (20 days ago)
• Topics: array, cuda, gpu, gpu-computing, multidimensional-arrays, pycuda, python, scientific-computing
• Language: Python
• Homepage: http://mathema.tician.de/software/pycuda
• Size: 2.92 MB
• Stars: 1,838
• Watchers: 56
• Forks: 284
• Open Issues: 86
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE
  • Citation: CITATION.cff

Awesome Lists containing this project

• awesome-list - PyCUDA - Pythonic Access to CUDA, with Arrays and Algorithms. (Linear Algebra / Statistics Toolkit / General Purpose Tensor Library)
• awesome-python-machine-learning-resources - GitHub - 27% open · ⏱️ 16.08.2022): (GPU实用程序)
• awesome-cuda-and-hpc - PyCUDA
• awesome-cuda-and-hpc - PyCUDA

README

        
PyCUDA: Pythonic Access to CUDA, with Arrays and Algorithms

=============================================================

.. image:: https://gitlab.tiker.net/inducer/pycuda/badges/main/pipeline.svg

    :alt: Gitlab Build Status

    :target: https://gitlab.tiker.net/inducer/pycuda/commits/main

.. image:: https://badge.fury.io/py/pycuda.png

    :target: https://pypi.org/project/pycuda

.. image:: https://zenodo.org/badge/1575319.svg

    :alt: Zenodo DOI for latest release

    :target: https://zenodo.org/badge/latestdoi/1575319

PyCUDA lets you access `Nvidia `_'s `CUDA

`_ parallel computation API from Python.

Several wrappers of the CUDA API already exist-so what's so special

about 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. PyCUDA knows about dependencies, too, so (for

  example) it won't detach from a context before all memory

  allocated in it is also freed.

* Convenience. Abstractions like pycuda.driver.SourceModule and

  pycuda.gpuarray.GPUArray make CUDA programming even more

  convenient than with Nvidia's C-based runtime.

* Completeness. PyCUDA puts the full power of CUDA's driver API at

  your disposal, if you wish. It also includes code for

  interoperability with OpenGL.

* Automatic Error Checking. All CUDA errors are automatically

  translated into Python exceptions.

* Speed. PyCUDA's base layer is written in C++, so all the niceties

  above are virtually free.

* Helpful `Documentation `_.

Relatedly, like-minded computing goodness for `OpenCL `_

is provided by PyCUDA's sister project `PyOpenCL `_.