Python is making inroads into the HPC landscape. However, writing Python code for efficient scientific computing is not entirely trivial. In this course a  variety of techniques and libraries will be discussed that are useful in this context. Subjects covered include profiling of code to discover opportunities  for optimization, using Cython, a Python extension that translate critical code sections into efficient C, wrapping C/C++/Fortran libraries in Python,  multithreaded/multiprocess Python, distributed programming use mpi4py, and pySpark for data science.

Target audience
This info session is primarily targeted at VSC-users, although other interested parties are welcome as well.

Prior knowledge required: 
Participants have programming experience in Python and preferably also in C/C++ or Fortran.

Result/Objectives
Participants can make an informed choice on various techniques to improve the performance of Python code, and know how to call C/C++ or Fortran functions from Python.