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GPU Architectures and Programming

The course is not on the list Without time-table
Code Completion Credits Range Language
NIE-GPU Z,ZK 5 2P+1C English
Garant předmětu:
Ivan Šimeček
Ivan Šimeček
Ivan Šimeček
Department of Computer Systems

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.


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).

Syllabus of lectures:

1. GPU microarchitecture.

2. (3) Programming language CUDA.

3. Fundamental parallel operations (reduction and prefix sum).

4. Methods of synchronization of threads and thread blocks.

5. Optimization I: General source code optimizations.

6. Optimization II: Architecture SIMT, sdruˇzen´y access to the shared memory.

7. Optimization III: Architecture memory subsystem.

8. Cooperation of multiple GPUs.

9. Asynchronous GPU computations.

10. Case studies of GPU programs. Development and debugging of GPU applications.

11. HPC libraries and other APIs for GPGPU.

Syllabus of tutorials:

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

Study Objective:
Study materials:

1. Kirk, D. B. : Programming Massively Parallel Processors (3rd Edition). Morgan Kaufmann, 2016. ISBN 978-0128119860.

2. Cheng , F. - Grossman, M. - McKercher, T. : Professional CUDA C Programming (1st Edition). Wrox, 2014. ISBN 978-1118739327.

3. Cook, S. : CUDA Programming: A Developer’s Guide to Parallel Computing with GPUs (1st Edition). Morgan Kaufmann, 2012. ISBN 978-0124159334.

Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2023-06-05
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