Parallelism and Concurrency

Learning Outcomes

  1. Recognize the types of parallelism in computer systems
  2. Recognize the models of execution in parallel systems.
  3. Recognize the concept of concurrecy and distinguish it from the concept of paralellism.
  4. Recognize the concepts of coherence, synchronization and memory models in parallel systems.
  5. Apply learned concepts to decompose simple problems for parallel execution.
  6. Apply learned concepts to performance optimizations of programs..

Forms of Teaching


Lectures, teaching materials available, theoretical and practical coverage of weekly topics.

Independent assignments

Project assignment covering the course topics.


Practical assignments covering the specific topic.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 25 % 50 % 25 %
Class participation 0 % 5 % 0 % 5 %
Seminar/Project 50 % 30 % 50 % 30 %
Final Exam: Written 50 % 30 %
Final Exam: Oral 10 %
Exam: Written 50 % 30 %
Exam: Oral 10 %

Week by Week Schedule

  1. Multiple simultaneous computations; Goals of parallelism (e.g., throughput) versus concurrency (e.g., controlling access to shared resources).
  2. Parallelism, communication, and coordination; Goals and basic models of parallelism.
  3. Shared Memory; Atomicity; Symmetric multiprocessing (SMP).
  4. Multicore processors; Shared vs; distributed memory.
  5. SIMD, vector processing; GPU, co-processing.
  6. Programming constructs for parallelism.
  7. Task-based decomposition.
  8. Midterm exam.
  9. Data-parallel decomposition.
  10. Programming errors not found in sequential programming.
  11. Models for parallel program performance.
  12. Evaluating communication overhead.
  13. Load balancing.
  14. Actors and reactive processes (e.g., request handlers).
  15. Final exam.

Study Programmes

University undergraduate
Computing (study)
Elective Courses (5. semester)
Electrical Engineering and Information Technology (study)
Elective Courses (5. semester)
University graduate
Computer Engineering (profile)
Profile obligatory course (1. semester)


John L. Hennessy, David A. Patterson (2017.), Computer Architecture, Morgan Kaufmann
Peter Pacheco (2011.), An Introduction to Parallel Programming, Elsevier
Ruud van der Pas, Eric Stotzer, Christian Terboven (2017.), Using OpenMP -- The Next Step, MIT Press
David R. Kaeli, Perhaad Mistry, Dana Schaa, Dong Ping Zhang (2015.), Heterogeneous Computing with OpenCL 2.0, Morgan Kaufmann

Laboratory exercises


ID 183376
  Winter semester
L2 English Level
L1 e-Learning
45 Lectures
0 Exercises
15 Laboratory exercises
0 Project laboratory

Grading System

90 Excellent
75 Very Good
65 Good
50 Acceptable