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, Technology 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:

    First cycle
    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.

    Second cycle
    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: 2018-08-30
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: week 24, 2019
  • Entry requirements: 120 credits in science/engineering. High Performance Programming, 10 credits (or Low-level Parallel Programming, 5 credits, and Scientific Computing and Calculus, 10 credits, where the latter may be included in the 120 credits).
    English language proficiency that corresponds to English studies at upper secondary (high school) level in Sweden ("English 6").
  • Responsible department: Department of Information Technology

Learning outcomes

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.

Content

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.

Instruction

Lectures, computer labs, assignments and project assignments.

Assessment

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.

Reading list

The reading list is missing. For further information, please contact the responsible department.