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.
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.
The software needed is in the virtual machine that you will need to download and run to complete this course. You will also need a local machine with 15GB free disk space and 2GB RAM.
Optionally, you can receive instructions to install the Python environment utilised in the course (Python, Numpy, Cython, mpi4py).
