Programování paralelních systémů

Garant předmětu:

Přednášející:

Cvičící:

Předmět zajišťuje:

katedra počítačových systémů

Anotace:

Nowadays, multi-core processors and GPU accelerators have become common components of computing clusters and high-performance computing systems, so knowledge and skills related to parallel programming are essential for every computer scientist. The aim of this course is to introduce students to the architectures and programming methods of parallel computers with shared memory, GPU accelerators, or with distributed memory. To effectively use these modern computing systems, it is essential to combine parallelization techniques at all three levels. Students will gain knowledge of the relevant programming models, languages and environments. They will become familiar with fundamental parallel algorithms and be able to analyze the limitations, efficiency, and scalability of parallel solutions to selected problems on high-performance computing systems. In addition to the necessary theory in lectures, students will gain practical experience and skills in programming in OpenMP, CUDA and MPI environments.

Požadavky:

Osnova přednášek:

1. Introduction to parallel systems.

2.OpenMP: Parallel programming for shared memory systems.

3. GPU programming.

4. MPI: Programming for distributed memory systems.

5. Efficiency of parallel algorithms.

6. Parallel reduction and prefix sum.

7. Parallel sorting I.

8. Parallel sorting II.

9. Parallel multiplication of polynomials and matrices.

10. Parallel dense solvers.

11. Parallel matrix-vector multiplication.

12. Parallel sparse solvers.

13. Synchronization and parallel IO.

Osnova cvičení:

Nutno doplnit.

Cíle studia:

Nowadays, multi-core processors and GPU accelerators have become common components of computing clusters and high-performance computing systems, so knowledge and skills related to parallel programming are essential for every computer scientist. The aim of this course is to introduce students to the architectures and programming methods of parallel computers with shared memory, GPU accelerators, or with distributed memory. To effectively use these modern computing systems, it is essential to combine parallelization techniques at all three levels. Students will gain knowledge of the relevant programming models, languages and environments. They will become familiar with fundamental parallel algorithms and be able to analyze the limitations, efficiency, and scalability of parallel solutions to selected problems on high-performance computing systems. In addition to the necessary theory in lectures, students will gain practical experience and skills in programming in OpenMP, CUDA and MPI environments.

Studijní materiály:

1. Barlas C.: Multicore and GPU Programming, 2nd Edition

Elsevier 2023

ISBN 978-0-12-814120-5

2. Kumar, V., Grama, A., Gupta, A., Karpis, G.: Introduction to Parallel Computing, 2nd Edition

Adison Wesley 2003

ISBN 0-201-64865-2

3. Mattson, T. G., Sanders, B. A., Massingill, B. L.: Patterns for Parallel Programming

Addison-Wesley Professional 2004

ISBN 978-0321940780

4. Miller, R., Boxer, L.: Algorithms Sequential and Parallel: A Unified Approach, 2nd Edition

Charles River Media 2005

ISBN 978-1-58450-412-2

5. Wilkinson, B., Allen, M.: Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers, 2nd Edition

Pearson 2004

ISBN 0136717101

6. Whu, W. W., Kirk, D. B., Hajj, I. E.: Programming Massively Processors, 4th Edition

Elsevier 2023

ISBN 978-0-323-91231-0

7. Zecevic, A. I.: Quantum and Parallel Computing

Scripta Aesthetica 2022

ISBN 979-8788504056

Poznámka:

Information about the course and another materials can be found at https://courses.fit.cvut.cz/QNIE-PPS.

Další informace:

https://courses.fit.cvut.cz/QNIE-PPS

Pro tento předmět se rozvrh nepřipravuje

Předmět je součástí následujících studijních plánů:

• Quantum Informatics (povinný předmět programu)