Python has gained tremendous popularity in scientific computing owing to the extensibility of the language, the rich ecosystem and being a convenient languages for scientists to develop and prototype codes productively. While using Python may offer many ways to achieve the same result, finding the most efficient means to do so for better performance and scalability can be a challenge. This course is focused on some of the ways to optimise and parallel scientific Python codes, encompassing subjects such as performance analysis, efficient use of scientific libraries (e.g. NumPy), extending Python to use more efficient languages (e.g. Cython) to carry out multi-processing using threads and the Message Passing Interface (MPI) on HPC systems.

The 2-day course, split over 4 afternoons, will cover the following topics:

Efficient use of NumPy
Performance Analysis
Using Cython and optimisations
Interfacing with external libraries
Multi-processing using threads
Parallel computing using mpi4py

Participation in the course is free of charge for both academia and industry.

This course is now fully booked. In order to cater for any additional demand, we have set up a waiting list for this course in case of cancellations, and potentially organise another edition in the next few months. Please register here to be placed on the waiting list.

Format

This course will be conducted remotely via Zoom (meeting details will be sent to registrants at the start of the course). It is split over the following sessions (4x afternoons):

Day 1: Tue 24 November 2020 - 1pm - 4.45pm (GMT)
Day 2: Fri 27 November 2020 - 1pm - 4.45pm (GMT)
Day 3: Tue 1 December 2020 - 1pm - 4.30pm (GMT)
Day 4: Fri 4 December 2020 - 1pm - 4.45pm (GMT)

You will only need a web browser to carry out the practical exercises.

Prerequisites

Each participant is expected to have a basic knowledge of Python, including:

syntax
basic data structures (lists, arrays, tuples, dictionaries)
control structures (if-else statements, for/while loops)
functions and modules

Some prior experience/knowledge of NumPy and the C programming language will be useful but not strictly required.
https://events.prace-ri.eu/event/1107/