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Python for HPC

Welcome to the MPCDF Python for HPC tutorial!

Authors

Max Planck Computing and Data Facility, Garching

Course material

  • The Jupyter notebooks discussed during the lectures are bundled into a Jupyter book for convenient reading.
  • This material is currently intended to be used only by the registered participants of a Python for HPC course given by the MPCDF
  • Redistribution requires the consent of the authors

List of topics

  • Introduction
    • Python refresher
    • Basic HPC
  • Efficient numerical computing
    • NumPy
    • SciPy
    • HDF5-based IO with h5py
    • Cython
    • Interfacing with C/C++, Fortran, CUDA, and libraries
    • Numba
    • Profiling
  • Parallel computing
    • Parallel computing basics
    • Python threading, GIL
    • multiprocessing
    • mpi4py
    • Running parallel Python programs with SLURM
  • Software engineering with Python
    • Testing
    • Packaging
    • Documentation
  • Visualization
    • matplotlib
    • Colors
  • Exercises and examples
    • Basic Python
    • NumPy
    • simple advection code
    • simple diffusion code (MPI)

References

This course is largely based on our experience from daily work. In addition, the following sources were used:

  • High Performance Python, Practical Performant Programming for Humans, Micha Gorelick, Ian Ozsvald, O'Reilly Media; 2014. (In particular, parts of the diffusion example are discussed similarly to the presentation in this book.)
  • A Whirlwind Tour of Python, Jake VanderPlas, O'Reilly Media, 2016.
  • official documentation of Python, NumPy, SciPy, Cython, Numba, mpi4py

Other minor sources are referenced in the notebooks.

Software prerequisites

Python packages

The examples discussed in this course are based on Python 3 and NumPy, SciPy, Cython, Numba, matplotlib, and few more. To conveniently get access to all those packages, you can e.g. download and install Anaconda Python or similar distributions.

Jupyter presentation via RISE

  • Presentation of the Jupyter notebook cells as slides is possible via the RISE extension.
    • Installation
      • pip install RISE
      • jupyter-nbextension install rise --py --sys-prefix
      • jupyter-nbextension enable rise --py --sys-prefix
    • To enter presentation mode, simply press <Alt+r> from within a notebook.