Practical Course: GPU Programming in Computer Vision (6h / 10 ECTS)
WS 2013/2014, TU München
Please direct ALL questions regarding this course at email@example.com.
Date and Location
Start: Mo, March 3, 2014, 10:00 – Lab 02.05.014
The course will take place in our lab 02.05.014. The general time is March 3–April 4, with the following timeline:
- 1 week with lectures and exercises (attendance mandatory): March 3–10
- 3 weeks project phase: March 11–28
- final presentation: between March 31–April 4
For more details see Course Layout below.
Note: There will be no course on March 4 (Faschingsdienstag).
The registration for this course has been closed. There are no more places available.
Requirements: Knowledge of C or C++, basic mathematics
Number of participants: up to 30
The goal of this course is to provide an introduction to the NVIDIA CUDA framework for massively parallel programming on GPUs.
During the implementation of basic computer vision algorithms students will gradually learn more how to harness the power of GPU computing.
Although we assume good knowledge of C or C++ and basic mathematics, no further prior knowledge about CUDA, or computer vision topics will be required.
During the course lecture students will learn how to program GPUs with CUDA. Afterwards the students will start to implement more sophisticated computer vision algorithms within a student project. The course finishes with a presentation and a live demo of the project results.
- Introduction to Parallel Computing
- Introduction to CUDA
- Implementation of basic computer vision algorithms with CUDA (e.g. convolution, diffusion)
- Student project: Implementation of an advanced computer vision application which uses CUDA acceleration for real-time processing of webcam images.
- Lecture (March 3–10): 2–3h lectures each day (attendance mandatory) from 10:00, followed by corresponding programming exercises until 18:00. The exercises must be done in groups of 2–3 students. The groups must be formed on the first day, March 3 (but you can decide on your team already beforehand, of course). You may leave early once you have finished the day's exercises.
- Project (March 11–28): Implementation of a student project in groups of 2–3 (same groups as in the lecture week). You are free to work from home if you like and all team members agree, but keep in mind that you will require CUDA-capable hardware, and should collaborate within your team. You should also prepare your final presentation during this time.
- Presentation and demo (March 31–April 4): Each group will be assigned a time slot on one of these days, to present their results and give a live demo, followed by a Q&A session.
Additional material can be downloaded from here.