Fundamentals of Accelerated Computing with CUDA Python
This workshop from Nvidia DLI, supervised by a certified instructor, teaches you fundamental tools and techniques to running GPU-accelerated Python applications using CUDA GPUs and the Numba compiler. You'll work through dozens of hands-on coding exercises and, the end of the training, implement a new workflow to accelerate a fully functional linear algebra program originally designed for CPUs observing impressive performance gains. After the workshop ends, you will have aditional resources to help you create new GPU-accelerated applications on your own.
Audience : Researchers, Engineers, PhD and post-docs
Duration : 8 hours
Date : June 5th, 2024 from 8.30 a.m. to 5.30 p.m.
Location: Ecole Polytechnique / Aile 0 / Jean Lascoux conference room
Maximum number of people : 20
Price : Free
Prerequisites :
- hardware: Laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated workstation in the Cloud.
- knowledge: 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.
Learning objectives :
At the conclusion of the workshop, you'll have an understanding of the fundamental tools and techniques for GPU-accelerated Python applications with CUDA and Numba :
- GPU-accelerate 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 bandwith.
Workshop language is English
Speaker : François Courteille (Nvidia certified instructor)
