Syllabus for High Performance Programming

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A revised version of the syllabus is available.

Syllabus

  • 10 credits
  • Course code: 1TD062
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computational Science A1N, Computer Science A1N, Technology 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: 2016-03-10
  • Established by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2016
  • Entry requirements: 120 credits in science/engineering including Scientific Computing II, 5 credits, a second course in computer programming and 30 credits in mathematics. Scientific Computing II may be replaced by Scientific Computing, bridging course or Numerical Methods and Simulation or Scientific Computing and Calculus.
  • Responsible department: Department of Information Technology

Learning outcomes

To pass, the student should be able to

  • implement computational algorithms to efficient C-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;
  • present written performance analysis in a clear and explicit way.

Content

Programming in C/C++ under Linux/Unix. Parallel programming with OpenMP and Pthreads. Task-based programming. Tools and methods for problem solving, software development, debugging and performance analysis. Different types of computer architectures and memory organisations. Efficient implementations of numerical methods on modern computer architectures. Applications from different areas in science and engineering.

Instruction

Lectures, computer labs, assignments and projects.

Assessment

Assignments and projects reported both as written reports and orally.

Syllabus Revisions

Reading list

Reading list

Applies from: Spring 2017

Some titles may be available electronically through the University library.