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Intensive Computation - Calcolo Intensivo

Annalisa Massini

Office Hours: appointment by email

Classes will regularly start on February 28th, 2018.

Aim of the course

The aim of the course is to provide students with methodologies for the solution of problems requiring intensive computation, in particular scientific problems.

Topics covered in this course include:

  • introduction to Matlab and to GPU architecture and programming
  • concepts and methods: sparse matrices, eigenvalues and eigenvectors, linear algebras methods, etc.
  • problems like Molecular Dynamics, Graph connectivity, Global search algorithms, etc.
  • discussion on architectures, arithmetic operations, introduction to GPU architecture and programming
  • errors and simulations.

Lectures 2017-2018

Lecture 1, February 28th, 2018 Introduction to the course Lecture 1 - Introduction
Lecture 2, March 2nd, 2018 Introduction to Matlab - Part 1 - Lecture 2 and 3 - Matlab - Laboratory March 2nd, 2018 Laboratory: Introductory exercises - Part 1
Lecture 3, March 7th, 2018 Introduction to Matlab - Part 2 - Lecture 2 and 3 - Matlab - Laboratory March 7th, 2018 Laboratory: Introductory exercises - Part 2
Lecture 4, March 9th, 2018 Compact storage methods for sparse matrix: Coordinate, Compact Sparse Row CSR, Compact Sparse Column CSC and Modified Sparse Row MSR, Block Sparse Row, Skyline, Diagonal, Ellpack-Itpack. Lecture 4 - Sparse Matrices
  • Appendix of book Solving Numerical PDEs: Problems, Applications, Exercises - Formaggia, Saleri, Veneziani - 2012 - read pp. 395-409
Lecture 5, March 14th, 2018 Lecture by Prof. Novella Bartolini. Introduction to boolean tomography of networks. Project proposals. Lecture 5 - Boolean Tomography & Project proposals - Prof. Bartolini
Lecture 6, March 16th, 2018 Discussion on exercises of Homework 1 and laboratory Homework 1 - Sparse Matrices - 2018-Homework 1 - Boolean Tomography
Lecture 7, March 21st, 2018 Lecture by Dr. Viviana Arrigoni. Linear systems. Gaussian elimination for solving systems of linear equations. Pivoting. Methods to avoid pivoting. Lecture 7 - Linear Systems Part 1
Lecture 8, March 23rd, 2018 Lecture by Dr. Viviana Arrigoni. Linear systems. Cholesky factorization. Jacobi iterative method. Gauss-Seidel iterative method. * Lecture 8 - Linear Systems Part 2

Some papers proposed for past exam projects - Articoli proposti per esami passati

Past year lectures


The exam consists of two parts:

  • Written exam. Students attending the lessons can take a mid-term exam and a final exam (or a whole exam). Mid-term and final exam (or whole exam) consist in a written test and exercises.
  • One of the following, at the choice of the student: oral exam/presentation of one-two papers/project.

There will also be homework assignments. Homeworks will contribute to the final grade.

Text of exams

Textbooks - Testi di riferimento

  • Introduction to High-Performance Scientific Computing, Lloyd D. Fosdick, Elizabeth R. Jessup, Carolyn J. C. Schauble and Gitta Domik, The MIT Press, 1996, ISBN 0-262-06181-3
  • Programming Massively Parallel Processors: A Hands-on Approach, David B. Kirk and Wen-mei W. Hwu, Morgan Kaufmann, 2010
  • Introduction to scientific computing: A Matrix-Vector Approach Using MATLAB, Charles F. Van Loan, Prentice Hall , 1997
  • Manuale di MATLAB. Consultare il sito: http://www.mathworks.com/help/techdoc/learn_matlab/bqr_2pl.html
  • Matlab. Concetti e progetti, Giovanni Naldi e Lorenzo Pareschi, Apogeo, 2007
  • Calcolo Scientifico: Esercizi e Problemi Risolti Con MATLAB e Octave, Alfio Quarteroni, Fausto Saleri, 2008


-- AnnalisaMassini

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Topic revision: r178 - 2018-03-23 - AnnalisaMassini

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