NVIDIA and the UWI FIC 5IR AI Conference are pleased to invite you to attend an upcoming hands-on technical training workshop:
Workshop Name:
Fundamentals of Accelerated Computing with CUDA Python
Date:
27th June 2025
Time:
TBA
Location:
Online
This training is offered exclusively to verifiable conference attendees, academic students, and staff. Instructions will be communicated via email once verified.
About This Workshop
This course explores how to use Numba—the just-in-time, type-specializing Python function compiler—to accelerate Python programs to run on massively parallel NVIDIA GPUs. You’ll learn how to: · Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs) · Use Numba to create and launch custom CUDA kernels · Apply key GPU memory management techniques Upon completion, you’ll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.
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 bandwidth
Topics Covered
The following topics and technologies are covered in this course:
CUDA programming general practices
Workshop Agenda (Delivered across both days)
Introduction
- Meet the instructor.
- Create an account at https://learn.nvidia.com/join
Introduction to CUDA Python with Numba
- Begin working with the Numba compiler and CUDA programming in Python.
- Use Numba decorators to GPU-accelerate numerical Python functions.
- Optimize host-to-device and device-to-host memory transfers.
Break (60 mins)
Custom CUDA Kernels in Python with Numba
- Learn CUDA’s parallel thread hierarchy and how to extend parallel program possibilities.
- Launch massively parallel custom CUDA kernels on the GPU.
- Utilize CUDA atomic operations to avoid race conditions during parallel execution.
Break (15 mins)
Multidimensional Grids, and Shared Memory for CUDA Python with Numba
- Learn multidimensional grid creation and how to work in parallel on 2D matrices.
- Leverage on-device shared memory to promote memory coalescing while reshaping 2D matrices.
Final Review
- Review key learnings and wrap up questions.
- Complete the assessment to earn a certificate.
- Take the workshop survey.
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
This workshop is brought to you by:
