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Parallel and Distributed Computing

Code 11485
Year 1
Semester S2
ECTS Credits 6
Workload PL(30H)/T(30H)
Scientific area Informatics
Learning outcomes To study models and techniques of parallel and distributed programming. To design and implement applications taking advantage of current multi-core architectures (CPU and GPU).
To study, analyse and use grid and cloud computing environments. At the end the student should be able to analyse scientific papers in the parallel and distributed computing area.
Syllabus The basic principles of parallel execution:
- Parallelism versus concurrency.
- Parallelism, communication and coordination.
- Programming errors not found in sequential programming.
Parallel decomposition
- Task-based decomposition
- Data-parallel decomposition
Parallel programming techniques
- Message-Passing
- Data parallelism
- Partitioning and divide-and-conquer strategies.
- Pipelined computations.
- Synchronous and Asynchronous computations.
- Load balancing and termination detection.
Parallel architectures
- Multi-core processors
- Shared versus distributed memory
- GPU, co-processing
Grid and Cloud computing
- Internet technologies and web services
Main Bibliography Concurrency, State Models & Java Programs. Jeff Kramer. John Wiley Sons, 2003.

Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers (2nd Edition). B. Wilkinson, M. Alle. Prentice-Hall, 2004

Programming Massively Parallel Processors, A Hands-on Approach. David B. Kirk and Wen-mei W. Hwu. Elsevier, 2010.

Heterogeneous Computing with OpenCL. B. Gaster, L. Howes, D. Kaeli and P. Mistry. Morgan Kauffman, 2011.

Grid Computing: Practical Guide to Technology & Applications, Ahmar Abbas, 2004

Mastering Cloud Computing, Rajkumar Buyya, Christian Vecchiola, and Thamarai Selvi.
Morgan Kaufmann, ISBN: 978-0-12-411454-8, 2013
Language Portuguese. Tutorial support is available in English.
Last updated on: 2019-07-09

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