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.