High-Performance Computing with Python

Content

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

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

Jan H. Meinke is a staff scientist at the Jülich Supercomputing Centre (JSC) and a member of the Simulation Laboratory Biology. He received his PhD in Physics in 2002 from Michigan State University and has been working at Forschungszentrum Jülich since 2005. His research interests include protein folding and finding ways to make efficient use of HPC hardware for solving scientific problems. He has been teaching Scientific Python courses since 2011.

Olav Zimmermann is a staff scientist at the Jülich Supercomputing Centre (JSC) and head of the Simulation Laboratory Biology. He received a diploma in experimental molecular genetics and a Ph.D. on a topic in structural bioinformatics from the University of Cologne and was a co-founder and director of the bioinformatics start-up Science Factory. Since 2005 he is at Forschungszentrum Jülich and has been teaching Scientific Python courses since 2011. Olav's main interests are synergies between machine learning and physics-based simulations, efficient analysis algorithms for biological data, and synthetic biology.

Agenda

Tuesday, July 02, 2019
10:15-10:30 – Welcome coffee
10:30-12:00 – Lecture
12:00-13:00 – Lunch break
13:00-14:30 – Lecture
14:30-15:00 – Coffee Break
15:00-16:30 – Lecture

Wednesday, July 03, 2019
09:00-10:30 – Lecture
10:30-10:45 – Coffee Break
10:45-12:15 – Lecture
12:15-13:15 – Lunch break
13:15-14:45 – Lecture
14:45-15:15 – Coffee Break
15:15-16:30 – Lecture

Thursday, July 04, 2019
09:00-10:30 – Lecture
10:30-10:45 – Coffee Break
10:45-11:30 – Lecture
11:30-12:00 – Machine room visit
12:00-13:00 – Lunch break
13:00-14:30 – Lecture
14:30-15:30 – Coffee Break
15:30-16:30 – Lecture

Registration

All participants must register for the course. The registration fee includes coffee breaks and lunches throughout the three day course.

Course Fee: 240 CHF

Deadline for registration: Monday, June 24, 2019

Kindly note that the course can take place only if there are sufficient confirmed registrations received by the deadline. The minimum number of participants is 8.

Please contact Guilherme Peretti-Pezzi (guilherme.peretti-pezzi@cscs.ch) for questions related to the course content and reception@cscs.ch for questions related to the event logistics. 

Kindly note that no parking space is available at the Swiss National Supercomputing Centre. The closest bus stop to the centre is Lugano, Stadio. From Lugano railway station, you should take bus number 4 or 6.

Suggestions regarding travels and accommodation are available here.

You are encouraged to travel by public transportation. There is a Park & Ride Resega parking lot, within five minutes walk from CSCS.

We look forward to welcoming you at CSCS!