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Lecturer(s)
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Juryca Karel, Ing. Ph.D.
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Pidanič Jan, doc. Ing. Ph.D.
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Course content
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1. Introduction and fundamentals of parallel programming. Comparing serial/parallel computation I. 2. Introduction and fundamentals of parallel programming. Comparing serial/parallel computation II. 3. Profiling, debugging, time measurement of code, identification of bottleneck 4. Standard optimizing techniques (independent on programming language) 5. Optimizing techniques for Matlab language 6. Vectorization of serial programs I. 7. Vectorization of serial programs II. 8. Parallel technique at Matlab system, parFor I. (programming techniques for Multi-Core and Cluster) 9. Parallel technique at Matlab system, parFor II. (programming techniques for Multi-Core and Cluster) 10. SPMD (Single Program Multiple Data), pmode (interactive parallel programming techniques) 11. Introduction to GPU computing 12. GPU computing at Matlab system 13. Benchmarking, testing, and analysis of parallel program
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing)
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Learning outcomes
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The course covers general principles of parallel programming in lectures. The course will be also focused on parallel processing hardware of graphics processing units (GPUs) and acceleration techniques at Matlab language.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Written examination
Student have to solve the problem and ability separately work on engaged problems, active participation in exercisings. Concrete requirements will students announced at first week semester.
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Recommended literature
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Hanselman D., Littfield B. Mastering Matlab. 2012. ISBN 0136013309.
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Kepner J. Parallel Matlab for Multicore and Multinode Computers. 2009. ISBN 978-0-89871-673-3.
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