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SF2568 Parallel Computations for Large- Scale Problems 7.5 credits

Advance course for giving a basic understanding of how to develop numerical algorithms and how these can be implemented on computers with distributed memory by using the message passing paradigm.

Information per course offering

Termin

Information for Spring 2027 Start 12 Jan 2027 programme students

Course location

KTH Campus

Duration
12 Jan 2027 - 31 May 2027
Periods

Spring 2027: P4 (4 hp), P3 (3.5 hp)

Pace of study

25%

Application code

12419

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Places are not limited

Target group
No information inserted
Planned modular schedule
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Schedule
Schedule is not published

Contact

Examiner
No information inserted
Course coordinator
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Teachers
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Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus SF2568 (Spring 2022–)
Headings with content from the Course syllabus SF2568 (Spring 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Basic ideas including hardware architectures, memory hierarchies, communications,
    parallelization strategies, measures of efficiency;
  • HPC and Green Computing;
  • Introduction to MPI, the Message Passing Interface;
  • Simple numerical algorithms including matrix operations, Gaussian elimination;
  • Algorithms on graphs including graph partitioning problems;
  • Parallel sorting;
  • More advanced parallel algorithms;
  • Standard libraries;

Intended learning outcomes

The goal of the course is to provide a basic understanding of how to develop algorithms and how to implement them in distributed memory computers using the message-passing paradigm.

After completion of the course components the student shall be able to:

  • select and/or develop algorithms and data structures for solving a given problem after having analyzed and identified properties of the problem which have the potential for an efficient parallelization;
  • theoretically analyze a given parallel algorithm with respect to efficiency and afterwards experimentally evaluate a program for parallel computing by running it on a high-performance computer;
  • implement a given algorithm on a distributed-memory computer using the message passing library MPI;
  • independently solve a more complex problem and present the results both orally and in writing in a scientific manner;
  • identify challenges of Green Computing in HPC.

Literature and preparations

Specific prerequisites

  • English B / English 6
  • Completed basic course in numerical analysis (SF1544, SF1545 or equivalent) and
  • Completed basic course in computer science (DD1320 or equivalent).

Recommended prerequisites

Basic programming skills, preferably in C, C++, Fortran. For those being comfortable with Java or Python a short introduction to C will be provided.

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

Examination and completion

Grading scale

A, B, C, D, E, FX, F

Examination

  • HEMA - Assignment, 4.5 credits, grading scale: A, B, C, D, E, FX, F
  • PROA - Project, 3.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

If the course is discontinued, students may request to be examined during the following two academic years.

Examiner

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Mathematics, Technology

Education cycle

Second cycle