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This entry is from Winter semester 2020/21 and might be obsolete. No current equivalent could be found.

CS 654 — Parallel and Distributed Algorithms
(dt. Parallele und verteilte Algorithmen)

Level, degree of commitment Specialization module, compulsory elective module
Forms of teaching and learning,
workload		 Lecture (2 SWS), recitation class (2 SWS).,
180 hours (60 h attendance, 120 h private study)
Credit points,
formal requirements		 6 CP
Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
Examination type: Oral or written examination
Language,
Grading		 German,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Data Science.
Duration,
frequency		 One semester,
Regelmäßig alle 3 bis 4 Semester
Person in charge of the module's outline Prof. Dr. Rita Loogen

Contents

After an introduction to the basic concepts of parallel processing, elementary parallel algorithms are discussed first. Subsequently, parallel algorithms for different problem classes such as sorting, matrix operations, graph procedures are treated. In addition, distributed basic procedures such as snapshot procedures, termination detection, garbage collection and procedures for distributed problems are presented.

In the accompanying exercises, different methods will be implemented in C / MPI (PVM) and in Eden (parallel Haskell).


Qualification Goals

  • Learning and classification of different basic patterns of parallel processing,
  • Comparison of different methods for parallel problem solving,
  • Creating parallel programs,
  • Practice of scientific working methods (recognition, formulation, solving problems, training of abstraction skills),
  • Training of oral communication skills in the exercises by practicing free speech in front of an audience and during discussion.

Prerequisites

None. The competences taught in the following module are recommended: Algorithms and Data Structures.


Applicability

Module imported from M.Sc. Data Science.

It can be attended at FB12 in study program(s)

  • B.Sc. Data Science
  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Business Informatics
  • M.Sc. Business Mathematics

When studying M.Sc. Business Mathematics, this module can be attended in the study area Specialization Modules.

The module is assigned to Computer Science. Further information on eligibility can be found in the description of the study area.


Recommended Reading

  • Grama, A. Gupta, G. Karypis, V. Kumar: Introduction to Parallel Computing, Pearson Education, 2003.
  • Joseph Jaja: An Introduction to Parallel Algorithms, Addison Wesley 1992
  • Gibbons, W. Rytter: Efficient Parallel Algorithms, Cambridge University Press 1988
  • M. Quinn: Parallel Programming in C with MPI and OpenMP, Mc Graw Hill 2003



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2020/21. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.