Official course description:

Full info last published 15/11-19
Course info
ECTS points:
Course code:
Participants max:
Offered to guest students:
Offered to exchange students:
Offered as a single subject:
Price for EU/EEA citizens (Single Subject):
10625 DKK
MSc. Master
MSc in Computer Science
Course manager
Associate Professor
Course semester
Forår 2020
27 January 2020
31 August 2020
Exam type
ekstern censur
Grade Scale
Exam Language

In this course, you will learn how to evaluate the performance of a computer system.

The course combines a focus on low-level system components (hardware, operating system, etc.) with the analysis of complex data systems.


In a world that requires near-instant response times and increasingly faster access to very large volumes of business-critical data, delays cost money.

Data scientists expect high-performance from their data systems in order to reduce time to insight.

Software and DevOps engineers are expected to continuously improve the performance of IT systems.

Oftentimes, performance profiles can uncover the effects of design or implementation bugs.

In this class, students will learn how to design and conduct performance experiments and how to troubleshoot existing complex data systems.

Formal prerequisites
Students should have taken introductory courses on database systems, operating systems, and C or C++ programming.
Intended learning outcomes

After the course, the student should be able to:

  • Design and conduct performance evaluation experiments
  • Formulate hypothesis about the causes of poor performance across different layers of a data system’s stack (i.e., data management components, operating system, file system, network, hardware).
  • Select appropriate set of tools for troubleshooting performance problems.
  • Analyze the performance of a complex real-world data system.
Learning activities

The course is composed of lectures and exercise sessions. The lectures will cover fundamental aspects of various systems components such as operating systems, storage systems, processors, data systems, etc. The exercises will introduce tools and methodology for troubleshooting and performance analysis. Students will work on a two-part project throughout the semester.

Course literature

The course literature is published in the course page in LearnIT.

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 10%
  • Lectures: 20%
  • Exercises: 20%
  • Project work, supervision included: 40%
  • Exam with preparation: 10%
Ordinary exam
Exam type:
D: Submission of written work with following oral, external (7-trinsskala)
Exam variation:
D22: Submission of written work with following oral exam supplemented by the work submitted.
Exam submission description:
Students submit a report based on the project. This report is hand-in for the exam.
Exam duration per student for the oral exam:
20 minutes

Time and date