- know actual technologies for parallel and cloud computations, methods and means for their development;
- be able to develop algorithms and programs for parallel computations on graphics processing units, to use opportunities of cloud services.

- prerequisite: Algorithmization and Programming
- co-requisites:

Parallel computing on graphics processing units (GPU). Architecture of a modern GPU. CUDA technology. CUDA C program structure. CUDA thread organization. CUDA memories. Performance of computations. Floating-point considerations. Parallel patterns. OpenCL technology. Opportunities for cloud computing. IaaS, PaaS, SaaS models. Migration to a cloud. Virtual machines for cloud infrastructure. Monitoring and management. Cloud applications.

- David B. Kirk, Wen-mei W. Hwu. Programming Massively Parallel Processors: A Hands-on Approach, Second Edition. - Morgan Kaufmann, 2012. – 518 p.
- Jason Sanders, Edward Kandrot. CUDA by Example: An Introduction to General-Purpose GPU Programming. - Addison-Wesley, 2010. – 310 p.
- Shane Cook. CUDA Programming: A Developer's Guide to Parallel Computing with GPUs. - Morgan Kaufmann, 2012. – 590 p.
- Rajkumar Buyya, James Broberg, Andrzej M. Goscinski (eds.) Cloud Computing: Principles and Paradigms. – Wiley, 2011. – 675 p.

- Current control (30%): written reports on laboratory works.
- Final control (70%) credit.