For decades now, most computers (and even most phones) have come with some kind of graphics accelerator, and for most of that time, most of us have used it just for creating game graphics. But GPUs are capable of much more than that: they're general-purpose compute engines that can be used to accomplish a whole range of tasks from optimisation to modelling.

Today, new technologies are going mainstream: computer vision systems, generative AI, large language models, and more can demand enormous compute resources, and GPUs are ideally placed to shoulder that load. Form small graphics processors on PCs, through complex processor engines with a few thousand cores, up to rented grids with tens of thousands of cores, GPUs represent a uniquely powerful technology for today's workloads.

But how do they work? How can you coordinate the efforts of tens of thousands of threads when it's hard enough coordinating a handful on a regular CPU?

In this session, Jules will introduce GPU programming. You'll learn:
- What is the GPU architecture, and why it is different from a regular CPU;
- What kinds of workloads are well-suited to GPU, and what aren't;
- The compiler and virtualisation technologies by which you can access GPUs: CUDA, OpenCL, and OpenGL;
- How to write, debug and validate your own kernels, using examples from graphics, data science, and machine learning.

By the end of this session, you'll be able to leverage the power of GPUs in your daily work.