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Main menu for Browse IS/STAG
Course info
KE / NOTZM
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Course description
Department/Unit / Abbreviation
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KE
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NOTZM
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Parallel Processing with Multimed. Sign.
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Form of course completion
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Examination
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Form of course completion
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Examination
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Long Title
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Parallel Processing with Multimedia Signals
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Accredited / Credits
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Yes,
4
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
1
[HRS/WEEK]
Seminar
3
[HRS/WEEK]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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Czech
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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0 / -
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3 / -
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0 / 0
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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not determined
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
0
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Evaluation scale |
A|B|C|D|E|F |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
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Evaluation scale |
A|B|C|D|E|F |
Evaluation scale for credit before examination |
S|N |
Substituted course
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KE/INPVE
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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The aim of the course is to acquaint students with the general principles of optimization, vectorization and the basics of parallel programming for the processing of multimedia signals (acoustic, video, radar, etc.).
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Requirements on student
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During the semester and in the final exam, the student must understand the solved problems and the ability to work independently on the assigned problems, active participation in seminars. Specific requirements will be communicated to students by teachers in the first week of the semester.
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Content
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1.Introduction to parallel programming, programming for multiple core processors and multiple processor stations (cluster)
2.Profiling, code debugging, tools for identifying problem areas in the code
3.Basic optimization techniques I. (clarity of the program, an order of operations, memory allocation, problem detection, time and memory requirements)
4.Optimization techniques II. (Finding unnecessary parts of the code in the program, internal functions, optimization of for-end cycles)
5.Vectorization and parallelization of the program (Use of matrix calculus and elimination of cycles)
6. Programming in Python language
7. Numerical calculations on multiple core processors
8.Parallel programming on multiple core processors in the Python I programming language.
9.Parallel programming on multiple core processors in the Python II programming language.
10.Parallel programming on graphics units (GPU)
11.Parallel programming on GPU I.
12.Parallel programming on GPU II.
13.CUDA in Python programming language
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Activities
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Fields of study
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V případě mimořádných opatření bude výuka probíhat vzdáleně s využitím programu MS Teams v době dle rozvrhu. Účast na schůzkách skupiny v MS Teams je ekvivalentní účasti na přednáškách a cvičeních.
In the case of distance learning, lessons will be tought trough MS Teams. Lessons will be at the time shown in the timetable. MS Teams is equivalent to participation and or attendens in lectures and excersises.
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Guarantors and lecturers
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Literature
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Basic:
SUH, Jung W. a Youngmin KIM. Accelerating MATLAB with GPU computing: a primer with examples.. Elseiver/Morgan Kaufman, 2014. ISBN 9780124080805.
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Basic:
Mathworks. Natick. MA: The Mathhworks, c1994-2019. 2019.
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Basic:
LUTZ, Mark a David ASCHER. Naučte se Python. Praha: Grada. Pohotová příručka, 2003. ISBN 80-247-0367-X.
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Basic:
PARKER, James. Python - An Introduction to Programming.. Herndon: Mercury Learning and Information., 2017. ISBN 978-1-9445346-5-3.
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Recommended:
TUAMANEN, Brain. Hands-On GPU Programming with Python and CUDA. Birmingham: Packt., 2018. ISBN 978-1-78899-391-3.
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Příprava na laboratorní měření, zpracování výsledků
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39
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Kontaktní výuka
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25
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Příprava na zkoušku
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30
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Účast na výuce
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26
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Total
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120
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Prerequisites - other information about course preconditions |
The student must have basic knowledge of the Matlab system (operations with matrices, vectors, indexing, cycle programming). |
Competences acquired |
Students will gain an overview of parallel programming tools, along with an overview of programming techniques for parallel programming, which can be generally used in any programming language. |
Teaching methods |
- Monologic (reading, lecture, briefing)
- Dialogic (discussion, interview, brainstorming)
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Assessment methods |
- Oral examination
- Written examination
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