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THERE IS NO FREE SPOT LEFT! WAITING LIST IS ALSO VERY CROWDED.

Please direct ALL questions regarding this course at cuda-ws1516@cvpr.in.tum.de.

Tutors: Thomas Möllenhoff, Robert Maier, Lingni Ma, Caner Hazirbas

Start: March 2016

The course will take place in our lab 02.05.014. Timeline will be announced very soon, with the following schedule:

• 1 week with lectures and exercises (attendance mandatory)
• 3 weeks project phase
• final presentation

For more details see Course Layout below.

Preliminary meeting will be on 06. July 2015 at 3pm. Classroom is our seminar room 02.09.023.

Slides for the pre-meeting can be found here

Only assigned students by matching system should register this practical course on TUMOnline.

Requirements: Knowledge of C or C++, basic mathematics

Number of participants: up to 24

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 15-18): 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 (but you can decide on your team already beforehand, of course). You may leave early once you have finished the present day's exercises.
  
• Project (March 19 - April 10): 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 (April 11 - 15): Each group will be assigned a time slot on one of the days, to present their results and give a live demo, followed by a Q&A session.
  

• NVIDIA CUDA Programming Guide

Additional material can be downloaded from here.