Parallel Computations on GPU

• 26 hrs lectures
• 12 hrs pc labs
• 14 hrs projects

• 60 pts final exam (written part)
• 10 pts mid-term test (written part)
• 30 pts projects

To familiarize yourself with the architecture and programming of graphics processing unit in the area of general purpose computuing using the NVidia libraries and OpenACC standard. To learn how to design and implement accelerated programs exploiting the potential of GPUs. To gain knowledge about the available libraries for programming on GPUs.
Knowledge of the parallel programming on GPUs in the area of general purpose computing, orientation in the area of accelerated systems, libraries and tools.  
Understanding of hardware limitations having impact on the efficiency of software solutions. 

Knowledge gained in courses AVS and partially in PRL and PPP.

• Current PPT slides for lectures
• Nvidia CUDA documentation: https://docs.nvidia.com/cuda/
• OpenACC documentation: https://www.openacc.org/
• Kirk, D., and Hwu, W.: Programming Massively Parallel Processors: A Hands-on Approach, Elsevier, 2010, s. 256, ISBN: 978-0-12-381472-2. download.
• Sanders, J., & Kandrot, E: CUDA by Example: An Introduction to General-Purpose GPU Programming. Review Literature And Arts Of The Americas. Addison-Wesley, 2010. download.
• Storti,D., and Yurtoglu, M.: CUDA for Engineers: An Introduction to High-Performance Parallel Computing, Addison-Wesley Professional; 1 edition, 2015. ISBN 978-0134177410. link.
• Chandrasekaran, S., and Juckeland, G.: OpenACC for Programmers: Concepts and Strategies,  Addison-Wesley Professional, 2017, ISBN 978-0134694283. link.

1. Architecture of graphics processing units.
2. CUDA programming model, tread execution.
3. CUDA memory hierarchy.
4. Synchronization and reduction.
5. Dynamic parallelism and unified memory.
6. Design and optimization of GPU algorithms.
7. Stream processing, computation-communication overlapping.
8. Multi-GPU systems.
9. Nvidia Thrust library.
10. OpenACC basics.
11. OpenACC memory management.
12. Code optimization with OpenACC.
13. Libraries and tools for GPU programming.

1. CUDA: Memory transfers, simple kernels
2. CUDA: Shared memory
3. CUDA: Texture and constant memory
4. CUDA: Dynamic parallelism and unified memory.
5. OpenACC: basic techniques.
6. OpenACC: advanced techniques.

• Development of an application in Nvidia CUDA
• Development of an application in OpenACC

Assessment of two projects, 14 hours in total and, computer laboratories and a midterm examination.

• Missed labs can be substituted in alternative dates.