Course details

Applications of Parallel Computers

PDD Acad. year 2020/2021 Winter semester

The course gives an overview of usable parallel platforms and models of programming, mainly shared-memory programming (OpenMP), message passing (MPI) and data-parallel programming (CUDA, OpenCL). A parallelization methodology is completed by performance studies and applied to a particular problem. The emphasis is put on practical aspects and implementation.


Deputy Guarantor

Language of instruction



Examination (written+oral)

Time span

39 hrs lectures

Assessment points

100 exam



Subject specific learning outcomes and competences

To learn how to parallelize various classes of problems and predict their performance. To be able to utilize parallelism and communication at thread- and process level. To get acquainted with state-of-the-art standard interfaces, language extensions and other tools for parallel programming (MPI, OpenMP). Write and debug a parallel program for a selected task.

Generic learning outcomes and competences

Parallel architectures with distributed and shared memory,  programming in C/C++ with MPI and OpenMP, GPGPU, parallelization of basic numerical methods.

Learning objectives

To clarify possibilities of parallel programming on multi-core processors, on clusters and on GP GPUs. View over synchronization and communication techniques. Get to know a method of parallelization and performance prediction of selected real-world applications, the design of correct programs and the use parallel computing in practice.

Topics for the state doctoral exam (SDZ):

  1. Indicators and laws of parallel processing, function of constant efficiency, scalability.
  2. Parallel processing in OpenMP, SPMD, loops, sections, tasks and synchronization primitives.
  3. Architectures with shared memory, UMA, NUMA, cache coherence protocols.
  4. Blocking and non-blocking pair-wise communications in MPI.
  5. Collective communications in MPI, parallel input-output.
  6. Architecture of superscalar processors, algorithms for out-of-order instruction execution.
  7. Data parallelism SIMD and SIMT, HW implementation and SW support.
  8. Architecture of graphics processing units, differences from superscalar CPUs. 
  9. Programming language CUDA, thread and memory models..


Prerequisite kwnowledge and skills

Types of parallel computers, programming in C/C++, basic numerical methods

Study literature

Fundamental literature

  • David Patterson John Hennessy: Computer Organization and Design MIPS Edition, Morgan Kaufmann, 2013, p. 800, ISBN: 978-0-12-407726-3
  • Victor Eijkhout: Introduction to High Performance Scientific Computing, 2015, 978-1257992546
  • Kirk, D., and Hwu, W.: Programming Massively Parallel Processors: A Hands-on Approach, Elsevier, 2017, p. 540, ISBN: 978-0-12-811986-0

Syllabus of lectures

  • Parallel computer architectures, performance measures and their prediction
  • Patterns for parallel programming
  • Synchronization and communication techniques.
  • Shared variable programming with OpenMP
  • Message-passing programming with MPI
  • Data parallel programming with CUDA/OpenCL
  • Examples of task parallelization and parallel applications

Controlled instruction

Defence of a software project based on a variant of parallel programming.

Course inclusion in study plans

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