Pyculib - Python bindings for CUDA libraries
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seibert Merge pull request #14 from dee6600/patch-2
addd CUDA-accelerated functions link
Latest commit 9744803 Feb 22, 2018



Pyculib provides Python bindings to the following CUDA libraries:

These bindings are direct ports of those available in Anaconda Accelerate.

Documentation is located here


The easiest way to install Pyculib and get updates is by using the Anaconda Distribution

#> conda install pyculib

To compile from source, it is recommended to create a conda environment containing the following:

  • cffi
  • cudatoolkit
  • numpy
  • numba
  • pyculib_sorting
  • scipy

for instructions on how to do this see the conda documentation, specifically the section on managing environments.

Once a suitable environment is activated, installation achieved simply by running:

#> python install

and the installation can be tested with:

#> ./


Documentation is located here.

Building Documentation

It is also possible to build a local copy of the documentation from source. This requires GNU Make and sphinx (available via conda).

Documentation is stored in the doc folder, and should be built with:

#> make SPHINXOPTS=-Wn clean html

This ensures that the documentation renders without errors. If errors occur, they can all be seen at once by building with:

#> make SPHINXOPTS=-n clean html

However, these errors should all be fixed so that building with -Wn is possible prior to merging any documentation changes or updates.