First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
MATHEMATICAL ENGINEERING
Course unit
NUMERICAL METHODS FOR HIGH PERFORMANCE COMPUTING
INP5070472, A.A. 2017/18

Information concerning the students who enrolled in A.Y. 2016/17

Information on the course unit
Degree course Second cycle degree in
MATHEMATICAL ENGINEERING - INGEGNERIA MATEMATICA (Ord. 2015)
IN2191, Degree course structure A.Y. 2015/16, A.Y. 2017/18
N0
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Degree course track MATHEMATICAL MODELLING FOR ENGINEERING AND SCIENCE [001PD]
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination NUMERICAL METHODS FOR HIGH PERFORMANCE COMPUTING
Department of reference Department of Civil, Environmental and Architectural Engineering
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge CARLO JANNA MAT/08

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses MAT/08 Numerical Analysis 6.0

Mode of delivery (when and how)
Period Second semester
Year 2nd Year
Teaching method frontal

Organisation of didactics
Type of hours Credits Hours of
teaching
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 26/02/2018
End of activities 01/06/2018

Syllabus
Prerequisites: Numerical Methods for Differential Equations
Target skills and knowledge: The present course aim at the student the basical concepts of scientific computing on high performance computers,the practical knowledge and experimentation of the main algorithms of parallel programming. During the course the most important and popular numerical kernels used in scientific programs will be deeply analyzed and studied.
Course unit contents: 1. Advanced numerical linear algebra: projection methods for non-symmetric systems (Bi-CG, QMR) and eigenproblems (Power Method, QR Method, Lanczos, DACG);
2. Multigrid;
3. Preconditioning techniques: ILU, approximate inverses, AMG;
4. Parallel numerical analysis: basic concepts, operations and communications, data structures;
5. Parallel programming paradigms: OpenMP and Message Passing Interface standards;
6. Parallel implementations: sparse linear algebra kernels, iterative methods, domain decomposition.
Textbooks (and optional supplementary readings)