logo-polimi
Loading...
Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2014/2015
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 095959 - ALGORITHMS AND PARALLEL COMPUTING
Docente Cremonesi Paolo
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) - MI (403) INGEGNERIA MATEMATICA* AZZZZ095959 - ALGORITHMS AND PARALLEL COMPUTING
088940 - ALGORITMI E CALCOLO PARALLELO
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. This part is mainly hands-on, and students 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 and graphs, dynamic programming) as well as simple scientific computing algorithms (matrix computations).
  • The second part covers data-intensive algorithms for information retrieval and data-mining problems. These algorithms adopt collaborative-filtering and content-based filtering techniques to facilitate search in large amounts of data. During the last years, big-data algorithms have seen an increasing adoption in various services. The most famous success story is Netflix. The Netflix company launched in 2009 a competition (www.netflixprize.com) offering a 1 million dollar prize to anyone able to create a better information retrieval system than the one adopted by Netflix itself. This part of the course presents several data-intensive algorithms, including the Netflix winner algorithm.
  • 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, OpenMP, and Cuda, three of the most widely used standards for writing portable parallel programs, each of them representative of a different programming paradigm. This part includes a significant programming component in which students program concrete examples from big-data domains such as data mining, information retrieval, recommender systems, 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

Please, visit http://hpc.paolocremonesi.org/exams


Bibliografia
Risorsa bibliografica obbligatoriaOfficial web site http://hpc.paolocremonesi.org/
Risorsa bibliografica obbligatoriaStanley B. Lippman, Josée Lajoie e Barbara E. Moo, C++ Primer, Editore: Addison-Wesley, Anno edizione: 2012, ISBN: 9780321714114
Risorsa bibliografica obbligatoriaPeter S. Pacheco, An Introduction to Parallel Programming http://www.amazon.it/Introduction-Parallel-Programming-Peter-Pacheco/dp/0123742609
Risorsa bibliografica facoltativaDavid B. Kirk, Wen-mei W. Hwu, Programming Massively Parallel Processors: A Hands-on Approach, Editore: Morgan Kaufmann http://www.amazon.it/Programming-Massively-Parallel-Processors-Hands/dp/0123814723

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
schedaincarico v. 1.6.5 / 1.6.5
Area Servizi ICT
11/08/2020