Skip to main content

DD2360 Applied GPU Programming 7.5 credits

DD2360 Applied GPU Programming

Choose semester and course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.


For course offering

Autumn 2024 appgpu24 programme students

Application code


Headings with content from the Course syllabus DD2360 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course focuses on three main questions:

  • 1. GPU architecture. Computation and memory organisation of different commercial graphics processors will be introduced. A comparison with conventional CPUs and a presentation of new future GPUs will be given.
  • 2. GPU programming with CUDA. CUDA's concepts and how to use them to develop applications for GPUs will be introduced through examples from different areas, such as image processing and scientific computing. Also development tools, such as debuggers and tools to measure performance will be presented.
  • 3. GPU programming with GPU libraries and algorithmic packages. Packages for high productivity, for example the Thrust library, OpenACC and cuDNN will be presented. Different packages will be explained through examples from different areas of computer science.

The students will obtain access to the GPU cluster Tegner at PDC if they do not have access to a computer with GPU already.

Intended learning outcomes

On completion of the course, students should be able to:

  • analyse GPU architecture, assess their advantages and identify potential software optimisations based on knowledge of the GPU architecture,
  • design and implement a programme for a GPU for applications in scientific computing, machine learning, image and video processing, computer graphics or for a mobile phone,
  • use experimental highly productive methods for GPU programming, such as GPU libraries and algorithmic packages, to speed up the development of large GPU applications,
  • use efficient development tools for GPU programming such as debuggers and tools for measuring performance,
  • prepare a written report about design development and the implementation of code for a GPU (with application to scientific computing, machine learning, image and video processing, computer graphics or for a mobile phone) and present the report at a seminar orally.

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

No information inserted


No information inserted


A series of articles that present design and implementation of applications for GPU will be published on the course web page. A book that partly covers the course content is "CUDA left Engineers" by D. Storti and M. Yurtoglu.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • LAB1 - Laboratory, 1.0 credits, grading scale: P, F
  • LAB2 - Laboratory, 1.0 credits, grading scale: P, F
  • LAB3 - Laboratory, 1.0 credits, grading scale: P, F
  • PRO1 - Project, 4.5 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted


Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted


Ivy Bo Peng, e-post:

Supplementary information

In this course, the EECS code of honor applies, see: