The Python programming language has become very popular in scientific computing for various reasons. Users not only implement prototypes for numerical experiments on small scales, but also develop parallel production codes, thereby partly replacing compiled languages such as C, C++, and Fortran. However, when following this approach it is crucial to pay special attention to performance. This course teaches approaches to use Python efficiently and reasonably in a HPC environment.
The first lecture gives a whirlwind tour through the Python programming language and the standard library. In the following, the lectures strongly focus on performance-related topics such as NumPy, Cython, Numba, compiled C- and Fortran extensions, profiling of Python and compiled code, parallelism using multiprocessing and mpi4py, parallel frameworks such as Dask, and efficient IO with HDF5. In addition, we will cover topics more related to software-engineering such as packaging, publishing, testing, and the semi-automated generation of documentation. Finally, basic visualization tasks using matplotlib and similar packages are discussed.
Level: Intermediate
Language: English
Price and Eligibility: Refer to the registration page for each event (generally free of charge for members of academia from Europe).
- Python programming experience
- Familiarity with the Linux command line
- 2023, Jul 25-27: three-day online course in collaboration with MPCDF
For an overview of all NHR@FAU courses, visit the course overview page.