This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® GPUs and the Numba compiler.
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
- GPU-accelerated NumPy ufuncs with a few lines of code.
- Configure code parallelization using the CUDA thread hierarchy.
- Write custom CUDA device kernels for maximum performance and flexibility.
- Use memory coalescing and on-device shared memory to increase CUDA kernel bandwidth.
Prerequisites
- Basic Python competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations
- NumPy competency, including the use of ndarrays and ufuncs
- No previous knowledge of CUDA programming is required
Frequency
3 times a year
Duration
8 hours
Next course
4th December 2025 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 about the course on the NVIDIA webpages.