Syllabus for High Performance Programming
A revised version of the syllabus is available.
- 10 credits
- Course code: 1TD062
- Education cycle: Second cycle
Main field(s) of study and in-depth level:
Computational 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:
- 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
- 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
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
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.
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.
Lectures, computer labs, assignments and projects.
Assignments and projects reported both as written reports and orally.
- Latest syllabus (applies from Autumn 2023)
- Previous syllabus (applies from Spring 2023)
- Previous syllabus (applies from Spring 2019)
- Previous syllabus (applies from Autumn 2016)
Applies from: Spring 2017
Some titles may be available electronically through the University library.
An Introduction to Parallel Programming.
Burlington: Elsevier Science, 2011.
Optimizing software in C++: An optimization guide for Windows, Linux and Mac platforms
Technical University of Denmark,