lebedov / Scikit Cuda

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.. image:: https://raw.githubusercontent.com/lebedov/scikit-cuda/master/docs/source/_static/logo.png :alt: scikit-cuda

Package Description

scikit-cuda provides Python interfaces to many of the functions in the CUDA device/runtime, CUBLAS, CUFFT, and CUSOLVER libraries distributed as part of NVIDIA's CUDA Programming Toolkit <http://www.nvidia.com/cuda/>, as well as interfaces to select functions in the CULA Dense Toolkit <http://www.culatools.com/dense>. Both low-level wrapper functions similar to their C counterparts and high-level functions comparable to those in NumPy and Scipy <http://www.scipy.org>_ are provided.

.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.3229433.svg :target: http://dx.doi.org/10.5281/zenodo.3229433 :alt: 0.5.3 .. image:: https://img.shields.io/pypi/v/scikit-cuda.svg :target: https://pypi.python.org/pypi/scikit-cuda :alt: Latest Version .. image:: https://img.shields.io/pypi/dm/scikit-cuda.svg :target: https://pypi.python.org/pypi/scikit-cuda :alt: Downloads .. image:: http://prime4commit.com/projects/102.svg :target: http://prime4commit.com/projects/102 :alt: Support the project .. image:: https://www.openhub.net/p/scikit-cuda/widgets/project_thin_badge?format=gif :target: https://www.openhub.net/p/scikit-cuda?ref=Thin+badge :alt: Open Hub

Documentation

Package documentation is available at <http://scikit-cuda.readthedocs.org/>_. Many of the high-level functions have examples in their docstrings. More illustrations of how to use both the wrappers and high-level functions can be found in the demos/ and tests/ subdirectories.

Development

The latest source code can be obtained from <https://github.com/lebedov/scikit-cuda>_.

When submitting bug reports or questions via the issue tracker <https://github.com/lebedov/scikit-cuda/issues>_, please include the following information:

• Python version.
• OS platform.
• CUDA and PyCUDA version.
• Version or git revision of scikit-cuda.

Citing

If you use scikit-cuda in a scholarly publication, please cite it as follows: ::

@misc{givon_scikit-cuda_2019,
          author = {Lev E. Givon and
                    Thomas Unterthiner and
                    N. Benjamin Erichson and
                    David Wei Chiang and
                    Eric Larson and
                    Luke Pfister and
                    Sander Dieleman and
                    Gregory R. Lee and
                    Stefan van der Walt and
                    Bryant Menn and
                    Teodor Mihai Moldovan and
                    Fr\'{e}d\'{e}ric Bastien and
                    Xing Shi and
                    Jan Schl\"{u}ter and
                    Brian Thomas and
                    Chris Capdevila and
                    Alex Rubinsteyn and 
                    Michael M. Forbes and
                    Jacob Frelinger and 
                    Tim Klein and
                    Bruce Merry and
                    Nate Merill and
                    Lars Pastewka and
                    Li Yong Liu and
                    S. Clarkson and
                    Michael Rader and
                    Steve Taylor and
                    Arnaud Bergeron and
                    Nikul H. Ukani and
                    Feng Wang and
                    Wing-Kit Lee and
                    Yiyin Zhou},
    title        = {scikit-cuda 0.5.3: a {Python} interface to {GPU}-powered libraries},
    month        = May,
    year         = 2019,
    doi          = {10.5281/zenodo.3229433},
    url          = {http://dx.doi.org/10.5281/zenodo.3229433},
    note         = {\url{http://dx.doi.org/10.5281/zenodo.3229433}}
}

Authors & Acknowledgments

See the included AUTHORS <https://github.com/lebedov/scikit-cuda/blob/master/docs/source/authors.rst>_ file for more information.

Note Regarding CULA Availability

As of 2017, the CULA toolkit is available to premium tier users of Celerity Tools <http://www.celeritytools.com>_ (EM Photonics' new HPC site).

Related

Python wrappers for cuDNN <https://developer.nvidia.com/cudnn>_ by Hannes Bretschneider are available here <https://github.com/hannes-brt/cudnn-python-wrappers>_.

ArrayFire <https://github.com/arrayfire/arrayfire>_ is a free library containing many GPU-based routines with an officially supported Python interface <https://github.com/arrayfire/arrayfire-python>_.

License

This software is licensed under the BSD License <http://www.opensource.org/licenses/bsd-license.php>. See the included LICENSE <https://github.com/lebedov/scikit-cuda/blob/master/docs/source/license.rst> file for more information.