May 14, 2019 - by CSCS

Python is increasingly used in high-performance computing projects. It can be used either as a high-level interface to existing HPC applications and libraries, as embedded interpreter, or directly.

This course combines lectures and hands-on sessions. We will show how Python can be used on parallel architectures and how to optimize critical parts of the kernel using various tools.

The following topics will be covered:

  • Interactive parallel programming with IPython and Jupyter Notebooks
  • Profiling and optimization
  • High-performance NumPy
  • Just-in-time compilation with Numba
  • Distributed-memory parallel programming with Python and MPI
  • Bindings to other programming languages and HPC libraries
  • Interfaces to GPUs

Target Audience: This course addresses scientists with a working knowledge of NumPy who wish to explore the productivity gains made possible by Python for HPC.

Instructors: Dr. Jan Meinke, Jülich Supercomputing Centre & Dr. Olav Zimmermann, Jülich Supercomputing Centre.

For the agenda, additional information and registration, please visit the event page >