Methods of parallel calculations
Course: Applied Mathematics
Structural unit: Faculty of Computer Science and Cybernetics
Title
Methods of parallel calculations
Code
ДВС.1.08
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
8 Semester
Number of ECTS credits allocated
4
Learning outcomes
LO 11. To be able to apply modern technologies of programming and software development, software implementation of numerical and symbolic algorithms.
LO 13. To use specialized software products and software systems of computer mathematics in practical work.
LO 18. Communicate effectively about information, ideas, problems and solutions with specialists and society in general
PLO 21.1. Know the main sections of applied mathematics and computer science, to the extent necessary for mastering general professional mathematical disciplines, applied disciplines and the use of their methods in the chosen profession.
PLO 25.1. Be able to use computer systems to implement computational algorithms and mathematical modeling.
Form of study
Full-time form
Prerequisites and co-requisites
1. Successful completion of courses: numerical methods, linear algebra, programming, mathematical analysis.
2. Know: basic concepts and facts of mathematical analysis, linear algebra and numerical methods.
3. Be able to read literature in English and have basic skills of searching for information on the Internet.
Course content
1 Principles of building parallel computing systems.
2 Problems of mathematical modeling and methods of solving them based on the development and use of high-performance calculations on the latest computer systems.
3 Evaluation of computational complexity of parallel variants of algorithms.
4 Methods and computer algorithms of parallel data processing for multi-core computers with shared memory.
5 Highlighting the main characteristics of efficiency and acceleration of parallel algorithms. The main stages of the technological scheme of their construction.
6 Definition of tools and strategies for solving mathematical modeling problems for science and engineering problems.
7 Software and Hardware for multicore systems with shared memory.
8 Large-scale distributed computing systems. Problems of implementation of distributed computing.
9 Basic types and characteristics of communication systems for multi-node computer architectures.
10 Algorithms for research and solving basic problems of computational mathematics based on distributed computing.
11 Principles of algorithm development, efficiency research and algorithm acceleration.
12 Supercomputer systems, modern solutions and trends.
13 Application of information technologies based on distributed computing for science and engineering problems.
14 Principles and tools for creating parallel programs based on distributed computing.
15 Methods and information technologies for mathematical modeling based on technology grids, as a variant of distributed computing. Mathematical and software tools of computing grids.
16 Methods and information technologies for mathematical modeling based on cloud technologies, as a variant of distributed computing. Mathematical and software toolkit of cloud computing.
17 Application of grids and cloud computing for mathematical modeling of physical and technical processes. Mathematics and software.
Recommended or required reading and other learning resources/tools
1. Voevodin V.V., Voevodin Vl.V. Parallel-nye vychisleniia. – SPb.: BKhV-Peterburg, 2002.
– 608 s.
2. Khimich A.N., Molchanov I.N., Popov A.V., Chistiakova T.V., Iakovlev M.F. Parallel-nye
algoritmy resheniia zadach vychislitel-noi matematiki. – Kiїv: Nauk. dumka, 2010. – 198 s.
3. Gergel- V.P. Teoriia i praktika parallel-nykh vychislenii. BINOM. Labolatoriia znanii,
2007,423 s.
4. Quinn M.J. Parallel Computing. Theory and practice. – New York. – 1994. – 446 p.
5. Ortega D. M. Vvedenie v parallel-nye i vektornye metody resheniia lineinykh sistem /
Moskva: Mir, 1991. 367 s.
6. http://www.netlib.org/scalapack
Planned learning activities and teaching methods
Lectures, independent work
Assessment methods and criteria
The maximum number of points that can be obtained by a student: 100/60 points.
- semester assessment:
1. Modular control work 1: 30 points/18 points.
2. Project defense: 30 points/18 points.
- final evaluation (in the form of an exam):
- the maximum number of points that can be obtained by a student: 40 points;
- learning outcomes that will be evaluated: PH1.1, PH1.2, PH2.1;
- form of implementation and types of tasks: written.
Types of tasks: 4 written tasks.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline