My goal is to create a generic and fast GPU-based histogramming which can be used as a Python module. This is based on an implementation by NVIDIA. This module should be compatible with PISA (PINGU Simulation and Analysis). All goals in detail:
- Compare timings to numpy's
histogramddand be faster - Work on arrays that are already on GPU (requirement from PISA)
- Ability to move additional arrays to GPU (requirement from PISA)
- Should be used as a Python module
- Identify the GPU and open threads accordingly (and use best kernel)
- Create generic histograms for generic data (N dimensional data, different bins, M dimensional histogram etc.)
- Implement a test method to compare the results
- Support single and double precision (requirement from PISA)
You are going to need to have the following requirements installed in order to use gpu_hist.py:
- CUDA -- version > 5 recommended; on Ubuntu install via
nvidia-cuda-toolkit - PyCUDA -- install via pip
- NumPy -- install via pip
If you would like to create benchmark tests with main.py or plot some histograms, you need:
- matplotlib -- install via pip
- pandas -- install via pip
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