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Parallel Matrix Computations - NMNV532

Title:	Paralelní maticové výpočty
Guaranteed by:	Department of Numerical Mathematics (32-KNM)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2013 to 2016
Semester:	summer
E-Credits:	5
Hours per week, examination:	summer s.:2/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech, English
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	prof. Ing. Miroslav Tůma, CSc. RNDr. Jaroslav Hron, Ph.D.
Class:	M Mgr. MMIB > Povinně volitelné M Mgr. MOD M Mgr. MOD > Povinně volitelné M Mgr. NVM M Mgr. NVM > Povinně volitelné
Classification:	Mathematics > Numerical Analysis

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Annotation -

Last update: T_KNM (07.04.2015)

The goal of this course is to introduce parallel processing of basic computational cores that can be encountered in mathematical modeling as well as in scientific computing in general. These cores include, for example, basic operations with dense and sparse matrices and preconditioning of Krylov space methods. The course includes also elementary introduction into multigrid and domain decomposition methods.

Literature - Czech

Last update: prof. Ing. Miroslav Tůma, CSc. (08.10.2017)

A.Grama, G. Karypis, V. Kumar, A. Gupta. Introduction to Parallel Computing, 2nd edition, Addison Wesley, 2003.

J. Dongarra, I.S. Duff, D. Sorensen and H. A. van der Vorst. Solving Linear Systems on Vector and Shared Memory Computers, SIAM, 1991.

A. Toselli, O. Widlund. Domain Decomposition Methods - Algorithms and Theory. Springer Series in Computational Mathematics, Vol. 34, 2005

M. Heath, E. Ng, B. W. Peyton, Parallel Algorithms for Sparse Linear Systems, SIAM Review 33(1991), 420-460.

B. Smith, P. Bjorstad, W. Gropp. Domain Decomposition: Parallel Multilevel Methods for Elliptic

Partial Differential Equations, Cambridge University Press 2004

W.L. Briggs, van Emden Henson, S.F. Cormick. A Multigrid Tutorial, SIAM, 2000.

Y. Saad, Iterative Methods for Sparse Linear Systems, ěnd edition, SIAM, Philadelphia, 2003.

Syllabus -

Last update: T_KNM (07.04.2015)

1. Computational models for parallel architectures.

2. Basic parallel operations with dense and sparse matrices.

3. Preconditioning and preconditioned Krylov space methods.

4. Domain decomposition and multigrid methods.

5. Parallelization of direct methods for sparse matrices.