A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.
NI-GPU GPU Architectures and Programming Extent of teaching: 2P+1C Instructor: Šimeček I. Completion: Z,ZK Department: 18104 Credits: 5 Semester: L Annotation:
Students will gain knowledge of the internal architecture of modern massively parallel GPU processors. They will learn to program them mainly in the CUDA programming environment, which is already a widespread programming technology of GPU processors. As an integral part of the effective computational use of these hierarchical computational structures, students will also learn optimization programming techniques and methods of programming multiprocessor GPU systems.
Lecture syllabus:
1. GPU microarchitecture. 2. - 4. (3) CUDA programming language. 5. Basic parallel operations (reduction and prefix sum). 6. Methods of synchronization of fibers and fiber blocks. 7. Optimization I: general optimization of massively parallel codes 8. Optimization II: SIMT architecture, combined memory access. 9. Optimization III: Memory subsystem architecture. 10. Collaboration multiple GPUs. 11. Asynchronous GPU calculations. 12. Case studies of GPU programs, development, debugging of GPU applications 13. HPC libraries and other APIs for GPGPU. Seminar syllabus:
1) Introduction to the environment, assignment of term papers 2) Submission of sequential implementation 3) Compilation of GPU code, involvement of libraries 4) Working with code debugging tools and profiling tools 5) consultation on GPU implementation 6) submission of GPU implementation, credit Literature:
Brian Tuomanen "Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA" , Packt Publishing, 2018 Sudhakar Yalamanchili "GPU Architectures" https://ece8823-sy.ece.gatech.edu/David B. Kirk, Wen-mei W. Hwu: Programming Massively Parallel Processors: A Hands-on Approach. 1st ed., Morgan Kaufmann, 2010.
J. Sanders, E. Kandrot ''CUDA by Example: An Introduction to General-Purpose GPU Programming'' Requirements:
Basics of programming in C and C ++ (at the level of subjects BI-PA1 and BI-PA2), it is recommended to complete the subject Parallel and Distributed Programming (MI-PDP).
The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester NI-NPVS.2020 Design and Programming of Embedded Systems V 2 NI-PSS.2020 Computer Systems and Networks PS 2 NI-SPOL.2020 Unspecified Branch/Specialisation of Study VO 2 NI-MI.2020 Managerial Informatics V 2 NI-ZI.2020 Knowledge Engineering V 2 NI-SPOL.2020 Unspecified Branch/Specialisation of Study VO 2 NI-TI.2018 Computer Science V 2 NI-SI.2020 Software Engineering (in Czech) V 2 NIE-DBE.2023 Digital Business Engineering VO 2 NI-PB.2020 Computer Security V 2 NI-WI.2020 Web Engineering V 2 NI-SP.2020 System Programming V 2 NI-SP.2023 System Programming V 2 NI-TI.2023 Computer Science V 2 NI-TI.2020 Computer Science V 2
Page updated 23. 4. 2024, semester: Z/2024-5, Z,L/2022-3, Z/2019-20, Z,L/2021-2, Z,L/2023-4, L/2019-20, Z,L/2020-1, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška