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
• 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

This course is aimed at scientists who wish to explore the productivity gains made possible by Python for HPC.