Tentalabbet

TDDD56 · 6 credits · Advanced level (A1N) · Informationsteknologi, Datateknik, Datavetenskap, Medieteknik

Multicore and GPU Programming

Active · next start HT 2026

70%

passed the latest ordinary exam

TEN1 · 15 Jan 2026 · 43 students

67%

passed the ordinary exams, all sittings over the last 5 years

TEN1 · 180 students

89%

Harder than 89% of courses at advanced level

Compared with 285 courses at the same level, ordinary exams over the last three academic years. 70 students.

Part of 11 programmes

Exam

TEN1 Written examination

  • 5
  • 4
  • 3
  • U

Ordinary exams

67% passed · 180 students

  • 70%43
  • 63%27
  • 77%35
  • 56%36
  • 67%39

Re-exams

62% passed · 61 students

  • 60%5
  • 64%11
  • 80%10
  • 58%12
  • 64%11
  • 43%7
  • 60%5
  • Pass the re-exam: 62% (61 students), compared with 67% (180 students) on the ordinary exam
  • Get 4 or 5: 20% (241 students)
Show small sittings (3)

Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

  • 67%3
  • 100%3
  • 100%2

Other modules

Partial exams, hand-ins and labs. They are not part of the figures at the top.

Hand-in or lab

LAB1 Laboratory work

  • G

Main rounds

100% passed · 179 students

  • 100%45
  • 100%30
  • 100%33
  • 100%34
  • 100%37

Later rounds

No sittings with at least 5 students.

    Show small sittings (7)

    Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

    • 100%1
    • 100%2
    • 100%2
    • 100%2
    • 100%2
    • 100%4
    • 100%2

    About the course

    Aim

    Modern computers feature processors with multiple cores and powerful many-core based hardware accelerators such as graphics processing units (GPUs) that can be used for general-purpose computations (GPGPU or GPU Computing). The performance potential of such architectures can only be leveraged for speeding up applications if the code is properly parallelized and (re)written to exploit the specific architectural features. After this course, the student will:

    • be able to write code and re-write code for multicore and GPGPU architectures
    • understand parallel algorithms and data structures, and be able to analyze them
    • know general principles for parallel computing and techniques for parallelization.

    Content

    Introduction to multi-core, many-core and GPU architecture concepts. Theory of parallel computing. Theory of parallelization. Design and analysis of parallel algorithms. Survey of parallel programming language concepts. Thread programming for multicore computing. SIMD-programming and data-parallel programming. GPU-programming with OpenCL and/or CUDA. Non-blocking synchronization and transactional memory. Scheduling for multicore and operating system issues. Introduction to heterogeneous multicore and parallel DSP architecture concepts and programming.

    Examiner
    Christoph Kessler
    Scheduled hours
    60 h
    Main field of study
    Informationsteknologi, Datateknik, Datavetenskap, Medieteknik
    Syllabus on Studieinfo ↗