During the past ten years, Python has seen a meteoric rise in both popularity and demand not only in general purpose programming but also in scientific computing. Data science and machine learning further contributed to this trend. Despite Python's architectural shortcomings, even the field of High-Performance Computing (HPC), with its highly specific demands, profited from recent developments with various libraries and optimisations.

This course shows the interested participant how to put Python to use on an HPC cluster as the VSC, how to optimize the code to increase performance, how to parallelize code for single and multi-node deployment as well as GPU offloading. Participants will be introduced to the most practical tools and various indispensable libraries for HPC use cases.

After completion of this three-day course, participants are able to create the environment they need on an HPC cluster, optimize and parallelize their Python use-cases and submit their jobs for batch processing on a supercomputer.

The content is delivered online over a three-day period with a day off after each course day. This should enable participants to have enough time to revisit the covered topics and come up with questions on the next course day.