Course syllabus adopted 2026-02-26 by Head of Programme (or corresponding).
Overview
- Swedish nameHögprestandaberäkning
- CodeTMA883
- Credits7.5 Credits
- OwnerMPENM
- Education cycleSecond-cycle
- Main field of studyMathematics
- DepartmentMATHEMATICAL SCIENCES
- GradingUG - Pass, Fail
Course round 1
- Teaching language English
- Application code 20163
- Maximum participants150 (at least 10% of the seats are reserved for exchange students)
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0126 Written and oral assignments 7.5 c Grading: UG | 7.5 c |
In programmes
- MPCAS - Complex Adaptive Systems, Year 1 (compulsory elective)
- MPCAS - Complex Adaptive Systems, Year 2 (elective)
- MPDSC - Data Science and AI, Year 1 (compulsory elective)
- MPDSC - Data Science and AI, Year 2 (elective)
- MPENM - Engineering Mathematics and Computational Science, Year 1 (compulsory)
- MPPHS - Physics, Year 2 (elective)
Examiner
- Martin Raum
- Full Professor, Algebra and Geometry, Mathematical Sciences
Eligibility
General entry requirements for Master's level (second cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements
Course specific prerequisites
Basic courses in mathematics, numerical analysis, data structures, and programming in Python or equivalent.
Aim
One aim of the course is to provide an insight into computer architecture and the effect it has on the performance of computer programs. The course also aims to provide the student with tools and techniques for code optimization and for parallel programming.
Learning outcomes (after completion of the course the student should be able to)
- Organize programming and program execution on a remote computer using Linux command line tools.
- Use AI-systems within programming.
- Describe the basic features of CPUs and GPUs.
- Assess the influence of hardware and software on runtime performance.
- Write simple parallel programs using OpenMP, MPI or for GPUs.
- Demonstrate awareness and competence required to contribute to equality, equal treatment, and diversity in society (DEI).
Content
- Short introduction to C to the extent that is necessary for the computer labs.
- Linux command line tools.
- AI-use in programming.
- Hardware architecture.
- Code optimization and compiler flags.
- Parallel programming using OpenMP, Threads, MPI, and for GPUs.
- Lecture and an assignment about equality, equal treatment, and diversity (DEI).
Organisation
Lectures and computer assignments. The assignments, which make up a substantial part of the course, consist of several short exercises that illustrate how performance is affected by the choice of computer architecture, programming language, data structures etc. The programs should be written in C. Please see the course homepage for more information.
Literature
Lecture notes, manuals, and articles.
Examination including compulsory elements
The examination consists of computer based assignments and an assignment about DEI.The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.