07. Understanding Computational Complexity in Julia HPC 🚀

In this video we will briefly introduce some theory that helps us classify the resource requirements of our algorithms in a generic way. We discuss the idea of computational complexity, which is usually expressed as the "Big Oh" runtime / memory requirements of an algorithm.

This video marks the first part of our week on optimisation, which will start with some theory and end with practical tips for making your code execute faster.

This module was designed as an MPAGS (Midlands Physics Alliance Graduate School) module and aimed at postgraduates and early career researchers.

Timecodes:
00:00:00 Introduction
00:00:55 Bubble Sort
00:05:44 Big O
00:08:26 Common Complexities
00:10:40 Example Big O Calculation
00:13:27 Quicksort
00:14:46 Performance Comparison
00:16:36 Outro

Other videos on this topic:
https://www.youtube.com/watch?v=Q_1M2JaijjQ
https://www.youtube.com/watch?v=7NC-iyZ7vpQ

Useful links:
Git/GitHub for Researchers: https://www.youtube.com/watch?v=CuOmaUS1FnM
Course Information Website: https://jamiemair.github.io/mpags-high-performance-computing/overview/
Julia Documentation: https://docs.julialang.org/en/v1/
MPAGS Registration: https://warwick.ac.uk/fac/sci/physics/mpags/registration/
Course Notes: https://jamiemair.co.uk/courses/hpc