Students will have good understanding of existing parallel computer and parallel programming models. Students will be able to design and implement a parallel algorithm maintaining the desired quality properties. Students will have practical knowledge of basic programming tools for parallel program design and quantitative performance analysis.

1. describe parallel computation and parallel programming models
2. describe PRAM computer model
3. apply PRAM programming model in parallel programming
4. apply MPI technology in parallel program development
5. recognize phases of parallel algorithm design
6. combine parallel algorithm development elements
7. evaluate efficiency and scalability of parallel algorithms

Teaching is organized into two teaching cycles (15-week classes). First cycle consists of 7-week classes and midterm exam. Second cycle consists of 6-week classes and final exam.

Midterm exam. Final exam.

Consultation hours will be given at the first lecture

Programming tasks.

1. Parallel computer models. Parallel programming paradigms. Properties of parallel programs. Sequential to parallel program conversion.
2. MPI - Message Passing Interface standard
3. Synchronous shared memory parallel computer model (PRAM).
4. Sum of prefixes algorithm.
5. Asynchronous parallel computer (APRAM). Model and program complexity.
6. Designing parallel algorithms. Design phases.
7. Algorithm partitioning. Communication structure definition.
8. (Midterm)
9. Task agglomeration. Task to processor mapping.
10. Examples of parallel algorithms designs.
11. Evolutionary and genetic algoritms principles. Genetic algorithm parallelization. Parallel evolutionary algorithms.
12. Quantitative analysis of parallel algorithms. Algorithm performance definitions.
13. Parallel algorithm scalability analysis.
14. Development of modular parallel programs. Modular development support in MPI.
15. (Final exam)