Parallel and Distributed Algorithms

11M1220

Master

German

5.0

only winter term

1 semester

Parallel and distributed algorithms are used in many areas where large amounts of data need to be processed or where high computing power is required. Some examples are:

• Scientific simulations: Parallel algorithms are often used in science to perform complex simulations, e.g. in physics, chemistry, biology or geology. Such simulations often require the processing of large amounts of data and the solution of complex equations that can be distributed across multiple processors.
• Image and signal processing: Parallel algorithms are also used in image and signal processing, e.g. to compress, encrypt or analyse images. Such applications often require the processing of large amounts of data in real time, which is only possible with parallel algorithms.
• Database applications: Parallel algorithms can also be used in database applications, e.g. to quickly search or sort large amounts of data. Such applications often require the processing of large amounts of data in a short time, which is only possible with parallel algorithms.
• Artificial intelligence and machine learning: Parallel algorithms are also used in artificial intelligence and machine learning, for example to train neural networks or recognise patterns in data. Such applications often require the processing of large amounts of data and the solution of complex optimisation problems, which can be distributed across multiple processors.

Distributed algorithms are often used in applications that are distributed across multiple computers or networks. Some examples are:

• Cloud computing: Distributed algorithms are often used in cloud computing applications, for example to store, process or analyse data. Such applications often require the cooperation of several computers or servers connected to each other via a network.
• Internet of Things: Distributed algorithms are also used in the Internet of Things, for example to collect, process or analyse sensor data. Such applications often require the cooperation of several devices or sensors connected to each other via a network.
• E-commerce: Distributed algorithms can also be used in e-commerce applications, for example to process orders or analyse customer profiles. Such applications often require the cooperation of several servers or databases connected to each other via a network.

1. Introduction
2. Fundamentals
3. Algorithms
4. Programming parallel algorithms
5. Selected topics in parallel algorithms

The total workload for the module is 150 hours (see also "ECTS credit points and grading").

• Project Report, written or
• Written examination

• Field work / Experimental work

Classic algorithms and data structures

Students at Osnabrück University of Applied Sciences who have successfully completed this module will be familiar with key parallel and distributed algorithms. They will be able to design parallel and distributed algorithms based on typical parallel computer architectures. Students will acquire detailed knowledge of the current state of research in a specific topic.

• Uelschen, Michael

• Morisse, Karsten
• Uelschen, Michael
• Thiesing, Frank