Speed up Python programs using optimisation and parallelisation techniques
The Python programming language is popular in scientific computing because of the benefits it offers for fast code development. The performance of pure Python programs is often suboptimal, but there are ways to make them faster and more efficient.
On this course, you’ll find out how to identify performance bottlenecks, perform numerical computations efficiently, and extend Python with compiled code. You’ll learn various ways to optimise and parallelise Python programs, particularly in the context of scientific and high performance computing.
What topics will you cover?
- Performance challenges of Python programming language
- Performance analysis of Python programs
- Efficient numerical calculations with NumPy
- Using compiled code with Python
- Interfacing Python to libraries written in other programming languages
- Parallel programming with Python
When would you like to start?
Start straight away and learn at your own pace. If the course hasn’t started yet you’ll see the future date listed below.
Who is the course for?
The course is designed for Python programmers who want to speed up their codes. You should be familiar with the basics of the Python programming language.
Who developed the course?
Partnership for Advanced Computing in Europe (PRACE)
The Partnership for Advanced Computing in Europe (PRACE) is an international non-profit association with its seat in Brussels.