As part of the HAI-End project, Durham University has developed this set of revision materials to support training events on performance analysis in high-performance computing. These mini-lectures are designed to help participants quickly revisit essential terminology and foundational concepts whenever needed, ensuring that everyone remains on the same page during workshops and practical sessions.

You are encouraged to explore these lectures and accompanying exercises to deepen your understanding of key topics such as the von Neumann architecture, cache memory, vectorisation, Flynn's Taxononomy, MPI, GPUs, the Roofline model, shared memory parallel paradigms, and both strong and weak scaling.

This course is designed to be flexible, allowing you to build your own personalised learning pathway. You may start from any topic of interest. Based on your choice, the knowledge graph available will recommend prerequisite sessions you should be familiar with, as well as suggest subsequent sessions to continue your learning journey effectively.

By engaging with this content, you will strengthen your grasp of these fundamental building blocks, enabling a clearer and more comprehensive context for analysing the performance of your code.

Please click here to access the interactive knowledge graph and decide your own learning path

We are committed to making this course as accessible and inclusive as possible. All video lectures are accompanied by captions and full transcripts, allowing you to engage with the material in a way that best suits your learning preferences or access needs.

Whether you prefer to read along, revisit key points in written form, or require support for hearing impairments, these features are designed to ensure that everyone can participate fully in the course.

If you encounter any accessibility barriers or have suggestions for improvements, please don’t hesitate to get in touch.