| layout | page |
|---|---|
| title | Introduction to CUDA |
| permalink | /education/dundee/ |
This course is part of the Three day Introduction to CUDA and Deep Learning with GPUs training course provided by NVIDIA and Dell and taught by Dr Paul Richmond and Twin Karmakharm (Research software Engineering at the University of Sheffield). The CUDA aspect of this course will take place over the first days followed by Deep Learning training on day two.
{: .center}
The aim of the CUDA course is to provide a basic understanding of principles of CUDA GPU programming and GPU programming. Prior knowledge of CUDA or Parallel programming is not required. Previous knowledge of C/C++ is required in order to get the most out of the course. Familiarity with concepts such as pointers, arrays and functions is required. The course consists of approximately 2-3 hours of lectures and 3-4 hours of practical training each day.
- 09:00-09:30 - Architectures.pdf
- 09:30-10:15 - Introduction to CUDA.pdf
- 10:15-10:30 - Coffee Break
- 10:30-10:45 - Lab: Getting Started with Qwiklabs
- 10:45-12:30 - Lab: Lab01 - CUDA Basics Lab
- 12:30-13:30 - Lunch
- 13:30-14:00 - Dell Solutions
- 14:00-15:00 - Optimisation.pdf
- 15:00-15:15 - Coffee Break
- 15:15-16:45 - Lab: Lab 02 - CUDA Optimisation Lab
- 16:45-17:00 - Final Words and Next Steps
Optional Material:
In this full-day workshop, you will learn the basics of deep learning by training and deploying neural networks. Build the skill-set and toolbox you need to build your own deep learning solutions through hands-on projects. Learners will:
- Understand general terms and background of deep learning
- Implement common deep learning workflows such as Image Classification and Object Detection
- Manipulate training parameters to improve accuracy
- Modify internal layers of neural networks to adapt to new problems
- Deploy your networks to start solving real-world problems
Content level: Beginner
Pre-Requisites: Technical background and basic understanding of deep learning concepts