Did you write code in Python and is it not fast enough? Do you run code, but are you running out of memory? If you want your code to make optimal use of your computing resources (whether it is your own laptop or an HPC system), then parallellizing your code might be the solution.
In this course we show several ways of speeding up your program and making it run in parallel. We introduce the following modules:
threading(allows different parts of your program to run concurrently on a single computer with shared memory).daskmakes scalable parallel computing easy.numbaspeeds up your Python functions by translating them to optimized machine code.memory_profilemonitors memory performance.
Prerequisites:
- This is an intermediate level Python course.
- We expect familiarity with the command-line, and that you are comfortable working with a coding text editor (like for instance, VS Code).
- This is a two-day workshop (4 and 5 June), it is not possible to join only one day.