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CUDA GPGPU programming

academic year
The health crisis linked to the spread of Covid-19 constitutes grounds of force majeure constraining the University to adapt the assessment methods for some of the course units; Information on the new terms of these assessments will be provided to students through the usual information channels by Apris 26, 2021.

Course teacher(s)

Gauthier LAFRUIT (Coordinator)

ECTS credits


Language(s) of instruction


Course content

Typical image processing algorithms (box filtering, windowed filtering, integral images, matrix calculus, etc) will be revisited for parallel implementations in CUDA with thread processing patterns that properly exploit the Graphics Processing Unit’s (GPU) memory and system architecture.

Objectives (and/or specific learning outcomes)

By the end of the course, the student will have implemented 2D image processing algorithms in CUDA, targeting (near) real-time applications. Similarities with OpenCL will be presented.

Teaching methods and learning activities

The course follows a “learn by example” approach.

The exercises will prepare the students to the parallel implementation of a 2D image processing algorithm described in a scientific paper, e.g. depth estimation with stereo matching.

Contribution to the teaching profile

This teaching unit contributes to the following competences:

  • Mastering CUDA parallel programming

  • Parallel threading in 2D image processing

References, bibliography, and recommended reading

John Cheng, Max Grossman, Ty McKercher, "Professional CUDA C Programming", John Wiley & Sons, 2014.

Other information


Office ULB-Solbosch L3.119, Tél. 02/650 30 89, Email: gauthier.lafruit@ulb.ac.be


Method(s) of evaluation

  • Other

Evaluation: 30% mastering the scientific paper, 70% practical exercises+project.

The evaluation covers a report and an oral presentation.

Mark calculation method (including weighting of intermediary marks)

Evaluation: 30% mastering the scientific paper, 70% practical exercises+project.