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The aim of the course is to introduce students to selected techniques of design and analysis of effective computer algorithms. The aim of the course is to familiarize students with tools and philosophy of implementing mass-parallel algorithms for Graphic Processing Units GPUs.
After the course the student defines vanych concepts of the computational and memory complexities of computer algorithms. After completing the course the student gives key concepts of modern algorithm design strategies.
The student also explains the principles of selected algorithms built on the basis of the discussed strategies and is also able to make their analysis in the terms of computational complexity. The student knows and understands the concept of basic issues in the field of parallel and massively parallel processing. The student is able to design and implement massively parallel variants of algorithms for solving selected classes of problems.
Student has the ability to adapt known modern programming techniques to solve new problems. In addition, the student presents created solutions and operates in accordance with the approved schedule of works.
Learning outcome 1 – lecture examination and activity during laboratory excercises. Learning outcome 2 – lecture examination and activity during laboratory excercises.
Learning outcome 3 – lecture examination and activity during laboratory excercises. Learning outcome 4 – lecture examination and activity during laboratory excercises.
Learning outcome 5 – lecture examination and activity during laboratory excercises.
Algorytmy, struktury danych i techniki programowania. Wydanie V
Learning outcome 6 – lecture examination and activity during laboratory excercises. Learning outcome 7 – activity during laboratory excercises. The final grade is the average of lecture and laboratory final grades. The lecture and laboratory final grades must be at least at satisfactory level. The basis of assessment struktyry the lecture is one examination. The final laboratory grade is the average of partial notes for individual laboratory exercises.
The concepts of memory and computational complexity. Mean and pessimistic complexity. Examples of complexity estimations. Fast and effective algorithms.
Metodyki i techniki programowania [DSP AGH]
The design strategies of fast and effective algorithms: Examples of effective algorithms: Architecture of modern graphics accelerators based on selected microarchitectures streaming multiprocessor, warp, half-warp, core processors. Review of modern Strukthry microarchitectures. Basics of massively parallel processing: Introduction to CUDA library.
Description of selected programming tools. CUDA parallel programming in examples. Struktury danych i techniki programowania; Helion, Gliwice