FDD3360 Applied GPU Programming 7.5 credits

Tillämpad GPU-programmering

Offering and execution

Course offering missing for current semester as well as for previous and coming semesters

Course information

Content and learning outcomes

Course contents *

The course focuses on three main topics:

  • GPU architecture. The computing and memory systems of different commercial GPUs are introduced. A comparison with conventional CPU and presentation of new upcoming GPUs will be given.

  • GPU programming with CUDA. The CUDA concepts and how to use them to develop applications for GPU are introduced by making examples from different fields, such as image processing or scientific computing. Also development tools, such debuggers and performance monitoring tools are presented.

  • GPU programming with GPU libraries and frameworks. High-productivity computing frameworks, among which the Thrust library, OpenACC and cuDNN, are presented. Different frameworks will be explained by providing examples from different computer science areas.

Students will be given access to the GPU cluster, Tegner, at PDC if they do not have access to a computer with GPU.

Intended learning outcomes *

After passing the course, the student will be able to:

  • analyze the GPU architecture, assess its advantages and identify potential software optimizations based on the knowledge of the GPU architecture.

  • design and implement a computer code for GPU with application to scientific computing, machine learning, image and video processing, computer graphics or mobile programming.

  • experimental high-productivity approaches for GPU programming, such as GPU libraries and computing frameworks, to speed-up the development of large GPU applications.

  • use effectively development tools for GPU programming, such as debuggers and performance monitoring tools.

  • prepare a written report on the design, development and implementation of a code for GPU (with application to scientific computing, machine learning, image and video processing, computer graphics or mobile programming) and present orally the report during a seminar.

Course Disposition

The course focuses on three main topics: GPU architecture, GPU programming with CUDA, and GPU programming with GPU libraries and frameworks.

Literature and preparations

Specific prerequisites *

PhD students at KTH

Recommended prerequisites

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A series of articles will be published in the course webpage. A useful course book is CUDA for Engineers" by D. Storti och M. Yurtoglu.

Examination and completion

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

Grading scale *

P, F

Examination *

    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.

    The examination consists of:

    • LAB1 - Laboratory assignment, 1 , grade: P, F

    • LAB2 - Laboratory assignment,, 1, grade: P, F

    • LAB3 - Laboratory assignment, 1, grade: P, F

    • PRO1 - Project work, 4,5, grade: P, F

    The project report must include a "related work" section, presenting a literature survey of the topic.

    Other requirements for final grade *

    Passing grade on all sections: LAB1, LAB2, LAB3, PRO1

    Opportunity to complete the requirements via supplementary examination

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    Opportunity to raise an approved grade via renewed examination

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    Erwin Laure

    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 web

    Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

    Course web FDD3360

    Offered by

    EECS/Computational Science and Technology

    Main field of study *

    No information inserted

    Education cycle *

    Third cycle

    Add-on studies

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    Stefano Markidis

    Postgraduate course

    Postgraduate courses at EECS/Computational Science and Technology