Cluster Architectures and Computations

Course content

To introduce parallel computers as target platform for applications that require either much memory or large computing power, or both.

The purpose of the class is to provide the student an understanding and practical experience with cluster-computing. Cluster-computing is becoming known to the general scientific community, including industry, and is increasingly becoming an alternative to classic supercomputers. The class covers classic supercomputer architectures and how these are programmed, as well as how clusters may emulate these so that applications and programming-techniques from different supercomputers may be used on clustercomputers. The target is that students obtain a detailed understanding of the problems that surround clustercomputers, as well as their known solutions and the limitations of these solutions. Topics: Parallel supercomputer architectures, advanced topics in CPU architecture, communication technology and machine-topology. Parallel algorithms, parallel programming and scientific computing. Programming using; threads, parallel virtual machines, message passing interface, remote memory and distributed shared memory.


M.Sc. programme in physics
M.Sc. programme in computer science

Learning outcome

At the course completion the student should be able to:
1. Design and implement parallel applications
2. Design a cluster-computer for a specific purpose
3. Use threads for shared memory architectures
4. Use Message Passing Interface
5. Manage vectorization of operations

The overall purpose of this course is to enable the student to write high performance parallel applications on cluster-type architectures. In addition the successful candidate will become familiar with a number of classic parallel computer architectures and a set of high performance scientific applications.



The students will understand the challenges in addressing parallelization of applications and limitations of the available hardware. In addition the students should have an ingrained skeptic approach to commercially presented buzzwords and benchmarks and be able to objectively select the best platform for a given problem.



Lectures and written projects.

Notes and articles.

The student must be experienced with writing applications, especially applications for scientific modeling, simulation or data-processing.

7,5 ECTS
Type of assessment
Continuous assessment
The class is evaluated through a set of reports that is written throughout the class. The final grade is the average of the best 3 reports. Each of these 3 reports must be passed separately with a grade of at least 02.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
More internal examiners
Criteria for exam assessment

See Skills.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 178
  • English
  • 206