Date and Time
The course will be held online on October 7th from 9 am to 5 pm.
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
- A free NVIDIA developer account is required to access the course material. Please register prior to the training at https://learn.nvidia.com/join/.
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.
Certification
Upon successful completion of all course assessments, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.
Structure
Module 1 -- 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.
Module 2 -- 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.
Module 3 -- 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.
Language
The course will be held in English.
Instructor
Dr. Sebastian Kuckuk, certified NVIDIA DLI Ambassador.
The course is co-organised by NHR@FAU and the NVIDIA Deep Learning Institute (DLI).
Prices and Eligibility
The course is open and free of charge for participants from academia from European Union (EU) member states and countries associated under Horizon 2020.
Withdrawal Policy
Please only register for the course if you are really going to attend. No-shows will be blacklisted and excluded from future events. If you want to withdraw your registration, please send an e-mail to sebastian.kuckuk@fau.de.
Wait List
To be added to the wait list after the course has reached its maximum number of registrations send an e-mail to sebastian.kuckuk@fau.de with your name and university affiliation.
