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Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2017/2018
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 095959 - ALGORITHMS AND PARALLEL COMPUTING
Docente Ardagna Danilo
Cfu 10.00 Tipo insegnamento Monodisciplinare

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (Mag.)(ord. 270) - BV (478) NUCLEAR ENGINEERING - INGEGNERIA NUCLEARE*AZZZZ095959 - ALGORITHMS AND PARALLEL COMPUTING
Ing Ind - Inf (Mag.)(ord. 270) - MI (487) MATHEMATICAL ENGINEERING - INGEGNERIA MATEMATICA*AZZZZ095959 - ALGORITHMS AND PARALLEL COMPUTING

Programma dettagliato e risultati di apprendimento attesi

ALGORITHMS AND PARALLEL COMPUTING

Description

Historically, parallel computing has been considered to be the high-end of computing, and has been used to model difficult problems in many areas of science and engineering. Today, commercial applications provide an equal or greater driving force in the development of faster programs. These applications require the processing of large amounts of data in sophisticated ways. Data-intensive applications such as data mining, recommender systems, financial modelling and multimedia processing have implications on the design of algorithms and provide a new challenge for the modern generation of computing platforms. Parallel processing is the only cost-effective method for the fast solution of these big-data problems. The emergence of inexpensive parallel computers such as commodity desktop multiprocessors, graphic processors, and clusters of PCs has made parallel methods generally applicable, as have software standards for portable parallel programming.

This course provides the students with all the skills necessary to write efficient algorithms, able to solve large-scale problems on parallel computers. The emphasis is on teaching concepts applicable across a wide variety of problem domains, and transferable across a broad set of computer architectures.

 

Topics

The course is structured in four parts.

  • The first part of the course covers modern object-oriented programming (OOP) and introduces the fundamentals of the C++11 programming language. C++11 is used as the reference language through the rest of the course. Students will gain experience in designing simple but powerful object-oriented applications and in writing code using the C++11 language. Example problems covers both traditional computer science algorithms (sorting, searching, lists) as well as simple scientific computing algorithms (matrix computations, gradient descent).
  • The second part covers data-intensive algorithms for information retrieval and data-mining problems and will focus on Spark, the new open source framework for in memory big data computations which includes also an extensive machine learning library.
  • The third part covers the main aspects of parallel computing: parallel architectures, programming paradigms, parallel algorithms. Parallel architectures range from inexpensive commodity multi-core desktops, to general-purpose graphic processors, to clusters of computers, to massively parallel computers containing tens of thousands of processors. Students learn how to analyse and classify these architectures in terms of their components (processor architecture, memory organization, and interconnection network). Pros and cons of different parallel programming paradigms (e.g., functional programming, shared memory, message passing) are evaluated by means of simple case studies.
  • The fourth part of the course introduces MPI one of the most widely used standards for writing portable parallel programs. This part includes a significant programming component in which students program concrete examples from big-data and scientific domains, machine learning, and operations research.

Please refer to the course official web site for further details.

 

Prerequisites

Prerequisite is the knowledge of a programming language, preferentially C.


Note Sulla Modalità di valutazione

Written test and a possibly a project (only for student who got at least 27 in the written test)


Bibliografia
Risorsa bibliografica obbligatoriaOfficial web site https://apc201718.wordpress.com
Risorsa bibliografica obbligatoriaStanley B. Lippman, Jos©e Lajoie e Barbara E. Moo, C++ Primer, Editore: Addison-Wesley, Anno edizione: 2012, ISBN: 9780321714114
Note:

Used also within ADVANCED PROGRAMMING FOR SCIENTIFIC COMPUTING course

Risorsa bibliografica obbligatoriaPeter S. Pacheco, An Introduction to Parallel Programming http://www.amazon.it/Introduction-Parallel-Programming-Peter-Pacheco/dp/0123742609
Risorsa bibliografica obbligatoriaBjarne Stroustrup, Programming - Principles and Practice Using C++, Anno edizione: 2014, ISBN: 978-0321-992789

Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
60.0
esercitazione
40.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
0.0
laboratorio di progetto
0.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.2 / 1.6.2
Area Servizi ICT
04/06/2020