This workshop provides a comprehensive introduction to general-purpose GPU programming with CUDA. You'll learn how to write, compile, and run GPU-accelerated code, leverage CUDA core libraries to harness the power of massive parallelism provided by modern GPU accelerators, optimize memory migration between CPU and GPU, and implement your own algorithms. At the end of the workshop, you'll have access to additional resources to create your own GPU-accelerated applications.
Difficulty rating: ★★★★ Advanced
Who is it for?
- Both research staff and research students
- Developers, data scientists, and researchers looking to solve challenging problems with deep learning and accelerated computing
Summary of the topics covered
- Write and compile code that runs on the GPU
- Optimize memory migration between CPU and GPU
- Leverage powerful parallel algorithms that simplify adding GPU acceleration to your code
- Implement your own parallel algorithms by directly programming GPUs with CUDA kernels
- Utilize concurrent CUDA streams to overlap memory traffic with compute
- Know where, when, and how to best add CUDA acceleration to existing CPU-only applications
Prerequisites
Basic C++ competency, including familiarity with lambda expressions, loops, conditional statements, functions, standard algorithms and containers.
Frequency
2 times a year
Duration
12 hours (over 2 days)
Next course
Tuesday 25th (9:30 - 16:30) and Thursday 27th (9:30 - 15:00) November 2025 - attend both days.
Book here
Can't attend?
We don’t have online materials for this session, but the course will run again — so you’ll be very welcome to join next time. You can find more information here.