Organisation:
Martin Oswald
Frank Steinbrücker

Location: Lab 02.05.014
Time and Date:

• 6 days with 7-8 hours lectures and supervised practical training each day:
  
• Mo-Fr, March 11-15 and Mo, March 18, 2013
  
• After this week students will have time to finish their project independently and to prepare the project presentation (workload approx. 5 days).
  
• Final presentations are planned for the 1 week of April (4th and 5th).

The course will be held in English, if desired.

Course Registration:

• send an e-mail with your name, student id and major field of study to Martin Oswald. Please also indicate that you want to register for the course in WS2012/13.
• We will enroll all registered participants in TUMonline for the examination in this course. (Note: There is no course enrolment in TUMonline, only the course examination enrollment which we will do!)

The registration for this course has been closed. There are many students on the waiting list.

Note to avoid any confusion: This course takes place at the end of the winter semester 2012/13 during the semester break. It will also be credited to the winter semester 2012/13!

Start: Mo, March 11, 2013, 10:00h - Lab 02.05.014

Requirements: Knowledge in C, basic mathematics

Number of Students: up to 16

The goal of this course is to provide an introduction into the NVIDIA CUDA Framework with the C programming language extension. 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 the C language and basic mathematics, no further prior knowledge about CUDA, or computer vision topics will be required.

During the course 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 the presentation of the project results.

• Introduction to Parallel Computing
• Introduction to CUDA
• Implementation of Basic Algorithms with CUDA (e.g. convolution, diffusion)
• Student Project: real-time optical flow estimation, superresolution from a series of images

• 30 percent: basic implementations
• 50 percent: student project
• 20 percent: project presentation

• NVIDIA CUDA Programming Guide
• Lecture Slides on the CUDA Training web page