Syllabus for Parallel and Distributed Programming
Parallell och distribuerad programmering
- 5 credits
- Course code: 1TD070
- Education cycle: Second cycle
Main field(s) of study and in-depth level:
Computational Science A1F,
Computer Science A1F,
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:
- Revised: 2022-10-20
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2023
120 credits in science/engineering including Introduction to Scientific Computing or Scientific Computing I. Participation in High Performance Programming or participation in Low-level Parallel Programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Responsible department: Department of Information Technology
On completion of the course, the student should be able to:
- develop programs with distributed parallelism, parallel debugging included;
- construct parallel algorithms, i.e. identify parallelism in a given algorithm and implement it;
- analyse properties such as efficiency, speedup etc., of parallel algorithms;
- analyse performance of parallel algorithms.
Classification of parallel computers: different kinds of memory organisations, processors, networks and program control flow. Different kinds of parallelism. MPI (Message Passing Interface) programming and data partitioning. Parallelisation of fundamental algorithms in numerical linear algebra and scientific computing: matrix-vector multiplication, matrix-matrix multiplication, FFT (Fast Fourier Transform), N-body simulation, graph algorithms.
Lectures, computer labs, assignments and project assignments.
Assignments and project presented both as written reports and oral presentations.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
- Latest syllabus (applies from Autumn 2023)
- Previous syllabus (applies from Autumn 2022)
- Previous syllabus (applies from Spring 2019)
- Previous syllabus (applies from Spring 2016)
Applies from: Autumn 2023
Some titles may be available electronically through the University library.
An Introduction to Parallel Programming.
Burlington: Elsevier Science, 2011.