This repository contains several example of ROC GPU programming using Numba. It is a fork and update of examples written for HSA https://github.com/ContinuumIO/numba-hsa-examples.

WARNING: The examples here and the underlying compilation chain are both under development!

For the purposes of this README, the $ symbol indicates the command prompt.

• First, install the ROC stack from AMD as per the installation instructions provided here .

• Create and activate a conda environment (named e.g. amd_roc) as follows:

$ conda create -n amd_roc -c numba numba roctools jupyter bokeh statsmodels \
python=3 h5py -y
$ source activate amd_roc

• Note that users should be a member of the video group to have access to the GPU.

There are two examples working at present, both revolving around kernel density estimation. The first is a Jupyter notebook, multi_variate_kde_example.ipynb which can be launched with the following:

$ jupyter notebook numba_roc_examples/kerneldensity/

You can see the rendered notebook here. The AMD GPU implementation used in notebook can be found here.

The second is a bokeh application that can be launched following the instructions in the README.md of the numba_roc_examples/kde_bokeh directory.

Run the full test suite, it is expected that this will fail (still under development):

$ ./runtests.sh

Selectively run tests from subdirectories:

$ ./runtests.sh <subdirectory>

Example:

$ ./runtests.sh numba_roc_examples/kerneldensity