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Data Structures and Algorithms
Last Updated: 2026-02-05 15:41:56
Abstract
This course is about fundamental algorithm design paradigms (such as induction, divide-and-conquer, backtracking, dynamic programming), classic algorithmic problems (such as sorting and searching), and data structures (such as lists, hashing, search trees). Moreover, an introduction to parallel programming is provided. The programming model of C++ will be discussed in some depth.
Objective
An understanding of the design and analysis of fundamental algorithms and data structures. Knowledge regarding chances, problems and limits of parallel and concurrent programming. Deeper insight into a modern programming model by means of the programming language C++.
Content
Fundamental algorithms and data structures are presented and analyzed. Firstly, this comprises design paradigms for the development of algorithms such as induction, divide-and-conquer, backtracking and dynamic programming and classical algorithmic problems such as searching and sorting. Secondly, data structures for different purposes are presented, such as linked lists, hash tables, balanced search trees, heaps and union-find structures. The relationship and tight coupling between algorithms and data structures is illustrated with geometric problems and graph algorithms. In the part about parallel programming, parallel architectures are discussed conceptually (multicore, vectorization, pipelining). Parallel programming concepts are presented (Amdahl's and Gustavson's laws, task/data parallelism, scheduling). Problems of concurrency are analyzed (Data races, bad interleavings, memory reordering). Process synchronisation and communication in a shared memory system is explained (mutual exclusion, semaphores, monitors, condition variables). Progress conditions are analysed (freedom from deadlock, starvation, lock- and wait-freedom). The concepts are underpinned with examples of concurrent and parallel programs and with parallel algorithms. The programming model of C++ is discussed in some depth. The RAII (Resource Allocation is Initialization) principle will be explained. Exception handling, functors and lambda expression and generic prorgamming with templates are further examples of this part. The implementation of parallel and concurrent algorithm with C++ is also part of the exercises (e.g. threads, tasks, mutexes, condition variables, promises and futures).
Resources
Literature
Cormen, Leiserson, Rivest, and Stein: Introduction to Algorithms, 3rd ed., MIT Press, 2009. ISBN 978-0-262-03384-8 (recommended text) B. Stroustrup, The C++ Programming Language (4th Edition) Addison-Wesley, 2013. Maurice Herlihy, Nir Shavit, The Art of Multiprocessor Programming, Elsevier, 2012.
Learning Materials (Links)
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Semesterly recurring
Examination
- Type
- session examination
- Mode
- written 150 minutes
- Aids
- Four A4-pages (two A4-sheets of paper), either handwritten or 11 point
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| revision course / private study |
Data Structures and Algorithms
Self-study course. No presence required.
|
No time listed | 210 h semesterly |
Offered In
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Computational Biology and Bioinformatics Master (More informations at: )
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Course Units for Additional Admission Requirements (The courses below are only available for MSc students with additional admission requirements.)
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