Python is being 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 session. We will show how Python can be used on parallel architectures and how performance critical parts of the kernel can be optimized using various tools.

For using Python productively for parallel computing, these topics will be covered:

Interactive parallel programming with IPython
Profiling and optimization
High-performance NumPy and SciPy, numba
Distributed-memory parallel programming with Python and MPI
Bindings to other programming languages and HPC libraries
Interfaces to GPUs
This course is aimed at scientists who wish to explore the productivity gains made possible by Python for HPC.

Prerequisites: Experience with Python and NumPy

Application
Registrations are only considered until 31 May 2017 due to available space, the maximal number of participants is limited. Applicants will be notified, whether they are accepted for participitation.

Instructors: Dr. Jan Meinke, Dr. Olav Zimmermann, JSC

Contact
For any questions concerning the course please send an e-mail to [email protected]

https://events.prace-ri.eu/event/567/