Syllabus for High Performance and Parallel Computing

Högprestanda- och parallellberäkningar

  • 7.5 credits
  • Course code: 1TD064
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computational Science A1N, Data Science A1N, Computer Science A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2020-02-27
  • Established by:
  • Revised: 2022-02-28
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Spring 2023
  • Entry requirements:

    120 credits in science/engineering, of which 40 credits in computer science including 10 credits in programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Information Technology

Learning outcomes

On completion of the course the student shall be able to:

  • implement computational algorithms to efficient code for modern computer architectures,
  • use tools for performance optimisation and debugging,
  • propose and implement efficient performance optimisations,
  • identify factors that restrict parallelism in an algorithm or a program,
  • analyse opportunities for sharing and duplication of data within the memory hierarchy,
  • present written performance analysis in a clear and explicit way.


Introduction to high performance programming and hardware. Different types of computer architectures and memory organisations. Important concepts such as data distribution, load balancing, locking and syncronization.

Parallel programming with native operating system threading as well as OpenMP, including support for heterogeneous architectures. Analysing bandwith and latency when designing data flows. Shared memory synchronization concepts, including mutexes and atomic operations. Task based programming. Tools and methods for problem solving, software development, debugging, performance analysis and performance optimization.


Lectures, computer labs, assignments and projects.


Computer labs (2 hp), assignments (2 hp)  and projects (3.5 hp) reported both as written reports and orally.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other directives

The course cannot be included in the same degree as 1DL560, 1TD351 and 1TD062.

Syllabus Revisions

Reading list

The reading list is missing. For further information, please contact the responsible department.