This is source code that is either used in the presentation, or was developed to create it. There is some material not covered in the presentation as well.

Requirements

• Python version: at least 3.6
• Packages (names listed taht can be used with pip or conda to install):
  • cython
  • dask
  • numba
  • numpy
  • numexpr
  • scipy
  • matplotlib
  • mpi4py
  • pytables
  • jupyter
  • ipywidgets

What is it?

1. cython: illustrations of how to use Cython to speed up Python.
2. dask: examples of how to use dask for distributed computing.
3. interfacing-c-c++-fortran: illustrations of ctypes, SWIG and f2py to interface with C, C++ and Fortran code.
4. ising: example of speeding up a Python simulation by wrapping C++ code using SWIG.
5. mpi4py: illustrations of distributed programming using MPI.
6. multiprocessing: illustrations of multithreaded programming using multiprocessing.
7. numba: illustration of using the numba library.
8. profiling: some illustrations and how-to on profiling a Python application.
9. pyspark: illustrations of using PySpark.
10. hdf5: examples of parallel I/O using HDF5.
11. numpy-scipy: some numpy/scipy codes for benchmakring.
12. pypy: code to experiment with the Pypy interpreter.
13. file-formats: influcence of file formats on performance.
14. performance: general considerations about performance.
15. convolution: wrap up exercise to apply all techniques.

Note: the GPU code in this repository was moved to its own repository