Unifying Python/C++/CUDA memory: Python buffered array ↔️ `std::vector` ↔️ CUDA managed memory
OTHER License
Unifying Python/C++/CUDA memory: Python buffered array C++11 std::vector
CUDA managed memory.
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.. contents:: Table of contents :backlinks: top :local:
Why
Data should be manipulated using the existing functionality and design paradigms of each programming language. Python code should be Pythonic. CUDA code should be... CUDActic? C code should be... er, Clean.
However, in practice converting between data formats across languages can be a pain.
Other libraries which expose functionality to convert/pass data formats between these different language spaces tend to be bloated, unnecessarily complex, and relatively unmaintainable. By comparison, ``cuvec`` uses the latest functionality of Python, C/C++11, and CUDA to keep its code (and yours) as succinct as possible. "Native" containers are exposed so your code follows the conventions of your language. Want something which works like a ``numpy.ndarray``? Not a problem. Want to convert it to a ``std::vector``? Or perhaps a raw ``float *`` to use in a CUDA kernel? Trivial.
- Less boilerplate code (fewer bugs, easier debugging, and faster prototyping)
- Fewer memory copies (faster execution)
- Lower memory usage (do more with less hardware)
Non objectives
--------------
Anything to do with mathematical functionality. The aim is to expose functionality, not (re)create it.
Even something as simple as setting element values is left to the user and/or pre-existing features - for example:
- Python: ``arr[:] = value``
- NumPy: ``arr.fill(value)``
- CuPy: ``cupy.asarray(arr).fill(value)``
- C++: ``std::fill(vec.begin(), vec.end(), value)``
- C & CUDA: ``memset(vec.data(), value, sizeof(T) * vec.size())``
Install
Requirements:
Python 3.7 or greater (e.g. via Anaconda or Miniconda <https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html#anaconda-or-miniconda>
_, or via python3-dev
)
(optional) CUDA SDK/Toolkit <https://developer.nvidia.com/cuda-downloads>
_ (including drivers for an NVIDIA GPU)
.. code:: sh
pip install cuvec
Usage
See `the usage documentation <https://amypad.github.io/CuVec/#usage>`_ and `quick examples <https://amypad.github.io/CuVec/#examples>`_ of how to upgrade a Python C++ CUDA interface.
See also `NumCu <https://github.com/AMYPAD/NumCu>`_, a minimal stand-alone Python package built using CuVec.
External Projects
For integration into Python, C++, CUDA, CMake, pybind11, and general SWIG projects, see the external project documentation <https://amypad.github.io/CuVec/#external-projects>
.
Full and explicit example modules using the CPython API <https://github.com/AMYPAD/CuVec/tree/main/cuvec/src/example_cpython>
, pybind11 API <https://github.com/AMYPAD/CuVec/tree/main/cuvec/src/example_pybind11>
, and SWIG <https://github.com/AMYPAD/CuVec/tree/main/cuvec/src/example_swig>
are also provided.
Contributing
See `CONTRIBUTING.md <https://github.com/AMYPAD/CuVec/blob/main/CONTRIBUTING.md>`_.
Licence
~~~~~~~
|Licence| |DOI|
Copyright:
- `Casper O. da Costa-Luis <https://github.com/casperdcl>`_
- `Contributors <https://github.com/AMYPAD/cuvec/graphs/contributors>`_
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.. |Downloads| image:: https://img.shields.io/pypi/dm/cuvec?logo=pypi&logoColor=white
:target: https://pypi.org/project/cuvec
.. |Coverage| image:: https://codecov.io/gh/AMYPAD/CuVec/branch/main/graph/badge.svg
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.. |Version| image:: https://img.shields.io/pypi/v/cuvec.svg?logo=python&logoColor=white
:target: https://github.com/AMYPAD/CuVec/releases
.. |Py-Versions| image:: https://img.shields.io/pypi/pyversions/cuvec.svg?logo=python&logoColor=white
:target: https://pypi.org/project/cuvec