Title:

High Performance Computations

Code:VNV
Ac.Year:2019/2020
Sem:Summer
Curriculums:
ProgrammeField/
Specialization
YearDuty
IT-MSC-2MBI-Elective
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Compulsory-Elective - group M
IT-MSC-2MIN-Compulsory-Elective - group B
IT-MSC-2MIS-Elective
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Compulsory
IT-MSC-2MPV-Elective
IT-MSC-2MSK-Elective
MITAINADE-Elective
MITAINBIO-Elective
MITAINCPS-Elective
MITAINEMB-Elective
MITAINGRI-Elective
MITAINHPC1stCompulsory
MITAINIDE-Elective
MITAINISD-Elective
MITAINISY-Elective
MITAINMAL-Elective
MITAINMAT-Elective
MITAINNET-Elective
MITAINSEC-Elective
MITAINSEN-Elective
MITAINSPE-Elective
MITAINVER-Elective
MITAINVIZ-Elective
Language of Instruction:Czech
Private info:http://www.fit.vutbr.cz/study/courses/VNV/private/
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:2600260
 ExamsTestsExercisesLaboratoriesOther
Points:60200200
Guarantor:Zbořil František V., doc. Ing., CSc. (DITS)
Deputy guarantor:Šátek Václav, Ing., Ph.D. (DITS)
Lecturer:Šátek Václav, Ing., Ph.D. (DITS)
Instructor:Šátek Václav, Ing., Ph.D. (DITS)
Veigend Petr, Ing. (DITS)
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Schedule:
DayLessonWeekRoomStartEndLect.Gr.Groups
MonlecturelecturesA113 15:0016:501MIT 2MIT MMM
MonlecturelecturesA113 17:0017:501MIT 2MIT MMM
Moncomp.lablecturesN105 17:0018:501MIT 2MIT xx
Moncomp.lablecturesN105 19:0019:501MIT 2MIT xx
Tuecomp.lablecturesN203 12:0013:501MIT 2MIT xx
 
Learning objectives:
  To provide overview and basics of practical use of parallel and quasiparallel methods for numerical solutions of sophisticated problems encountered in science and engineering.
Description:
  The course is aimed at practical methods of solving sophisticated problems encountered in science and engineering. Serial and parallel computations are compared with respect to a stability of a numerical computation. A special methodology of parallel computations based on differential equations is presented. A new original method based on direct use of Taylor series is used for numerical solution of differential equations. There is the TKSL simulation language with an equation input of the analysed problem at disposal. A close relationship between equation and block representation is presented. The course also includes design of special architectures for the numerical solution of differential equations.
Subject specific learning outcomes and competencies:
  Ability to transform a sophisticated technical problem to a system of differential equations. Ability to solve sophisticated systems of differential equations using simulation language TKSL.
Generic learning outcomes and competencies:
  Ability to create parallel and quasiparallel computations of large tasks.
Why is the course taught:
  Supercomputers are often used to solve large technical and scientific problems. Before writing the first line of code, the user should perfectly understand the problem, that is being solved.

The goal of this course is to familiarize the students with the physics behind the problems, that are often solved in practice. To be able to see connection between the equations that govern the problem (and then solve it using differential calculus) and the real system. The students should also understand the numerical methods that are being used in the often used software packages as "black boxes". To be able to choose a proper numerical method for a specific problem and not just pick one at random.
Syllabus of lectures:
 
  1. Methodology of sequential and parallel computation (feedback stability of parallel computations)
  2. Extremely precise solutions of differential equations by the Taylor series method
  3. Parallel properties of the Taylor series method
  4. Basic programming of specialised parallel problems by methods using the calculus (close relationship of equation and block description)
  5. Parallel solutions of ordinary differential equations with constant coefficients, library subroutines for precise computations
  6. Adjunct differential operators and parallel solutions of differential equations with variable coefficients
  7. Methods of solution of large systems of algebraic equations by transforming them into ordinary differential equations
  8. The Bairstow method for finding the roots of high-order algebraic equations
  9. Fourier series and finite integrals
  10. Simulation of electric circuits
  11. Solution of practical problems described by partial differential equations
  12. Control circuits
  13. Conception of the elementary processor of a specialised parallel computation system.
Syllabus of computer exercises:
 
  1. Simulation system TKSL
  2. Exponential functions test examples
  3. First order homogenous differential equation
  4. Second order homogenous differential equation
  5. Time function generation
  6. Arbitrary variable function generation
  7. Adjoint differential operators
  8. Systems of linear algebraic equations
  9. Electronic circuits modeling
  10. Heat conduction equation
  11. Wave equation
  12. Laplace equation
  13. Control circuits
Syllabus - others, projects and individual work of students:
 Elaborating of all computer laboratories results.
Fundamental literature:
 
  • Kunovský, J.: Modern Taylor Series Method, habilitation thesis, VUT Brno, 1995
  • Hairer, E., Norsett, S. P., Wanner, G.: Solving Ordinary Differential Equations I, vol. Nonstiff Problems. Springer-Verlag Berlin Heidelberg, 1987.
  • Hairer, E., Wanner, G.: Solving Ordinary Differential Equations II, vol. Stiff And Differential-Algebraic Problems. Springer-Verlag Berlin Heidelberg, 1996.
  • Shampine, L. F.: Numerical Solution of ordinary differential equations, Chapman and Hall/CRC, 1994
  • Strang, G.: Introduction to applied mathematics, Wellesley-Cambridge Press, 1986
  • Meurant, G.: Computer Solution of Large Linear System, North Holland, 1999
  • Saad, Y.: Iterative methods for sparse linear systems, Society for Industrial and Applied Mathematics, 2003
  • Burden, R. L.: Numerical analysis,  Cengage Learning, 2015
  • LeVeque, R. J.: Finite Difference Methods for Ordinary and Partial Differential Equations: Steady-State and Time-dependent Problems (Classics in Applied Mathematics), 2007
  • Strikwerda, J. C.: Finite Difference Schemes and Partial Differential Equations,  Society for Industrial and Applied Mathematics, 2004
  • Golub, G. H.: Matrix computations, Hopkins Uni. Press, 2013
  • Duff, I. S.: Direct Methods for Sparse Matrices (Numerical Mathematics and Scientific Computation), Oxford University Press, 2017
  • Corliss, G. F.: Automatic differentiation of algorithms, Springer-Verlag New York Inc., 2002
  • Griewank, A.: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Society for Industrial and Applied Mathematics, 2008
  • Press, W. H.: Numerical recipes : the art of scientific computing, Cambridge University Press, 2007
Study literature:
 
  • Hairer, E., Norsett, S. P., Wanner, G.: Solving Ordinary Differential Equations I, vol. Nonstiff Problems. Springer-Verlag Berlin Heidelberg, 1987.
  • Hairer, E., Wanner, G.: Solving Ordinary Differential Equations II, vol. Stiff And Differential-Algebraic Problems. Springer-Verlag Berlin Heidelberg, 1996.
  • Butcher, J. C.: Numerical Methods for Ordinary Differential
  • Lecture notes written in PDF format,
  • Source codes (TKSL) of all computer laboratories
Controlled instruction:
  During the semester, there will be evaluated computer laboratories. Any laboratory should be replaced in the final weeks of the semester.
Progress assessment:
  Half Term Exam and Term Exam. The minimal number of points which can be obtained from the final exam is 29. Otherwise, no points will be assigned to a student.
 

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