Performance Profiling & Optimisation for Python

Is your research code (in Python) slower than you'd like it to be? Do you want to optimise your code performance, but you're not sure how? This introductory level one-day workshop run by King's College London e-Research will show you how to assess where time is being spent during execution of a Python program and introduce good practices for optimising your code. It will also provide a high level overview of how code executes and how this maps to the limiting factors of performance.

Target audience:

The course is aimed at graduate students, postdocs and other researchers who are comfortable writing Python code for their research workflows, but want to advance their programming knowledge and write more efficient code.

Learning objectives:

After attending this training, participants will be able to:

• identify the most expensive functions and lines of code using cprofile and line_profiler
• evaluate code to determine the limiting factors of its performance
• recognise and implement optimisations for common limiting factors of performance

Requirements:

Before joining this workshop, participants should:

• be able to implement basic algorithms in Python
• be able to read and follow the control flow of Python code, and dry run the execution in their head or on paper
• have some familiarity with command-line interfaces such as the Bash shell

Workshop format:

Learners will bring their own laptops (Windows/Unix/Mac). This will allow them to store notes and scripts, as well as enabling them to carry out hands-on work following the instructor.

Note: This registration page is for non-KCL attendees only. King's College London staff and students should register via Skillsforge.

Questions? Send an email to e-research-training@kcl.ac.uk.