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ARCHITECTURES AND MODELS FOR NUMERICAL METHODS

CODE 98217
ACADEMIC YEAR 2021/2022
CREDITS
  • 5 cfu during the 2nd year of 10728 ENGINEERING TECHNOLOGY FOR STRATEGY (AND SECURITY)(LM/DS) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR ING-INF/01
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 1° Semester
    MODULES This unit is a module of:
    TEACHING MATERIALS AULAWEB

    AIMS AND CONTENT

    LEARNING OUTCOMES

    The course tackles the design and implementation of numerical algorithms for high performance computers in order to let students have a practical experience of the subject. In this framework, advanced architectures, parallel numerical algorithms, and their application to scientific and engineering problems are considered.

    AIMS AND LEARNING OUTCOMES

    The course aims to show computational models and computer architectures to let students understand their issues and deal with the implementation of numerical algorithms onto high performance computers. A wide range of topics is addressed,  from advanced architectures, parallel numerical algorithms, and their application to scientific and engineering problems.

    TEACHING METHODS

    Lectures integrated by tutorials.

    Many algorithm examples in Python are provided and discussed during the lessons.

    SYLLABUS/CONTENT

    1. Review of basic computer architecture concepts: examples, hardware, architectural and and software levels, processors, basic system architecture, Von Neumann machines, CISC and RISC machines, digital signal processors (DSP), memory. Layered structure of a computer: from hardware to software. Design and architectural aspects. GPU architectures.
    2. Parallel Computing: parallel and distributed computers, multiprocessor architecture, shared-memory and message-passing architectures, interconnection networks, interprocessor communication, performance analysis. Parallel computer memory architectures. Communication complexity.
    3. Parallel thinking: parallel algorithm design, parallel programming models, parallel performance. Introduction to parallel and concurrent programming with python.
    4. Numerical models and algorithms:
      • Parallel algorithm examples: finding paralleism by a conceptual example, prime numbers algorithm, Montecarlo Pi estimator, array processing, simple heat equation.
      • Introducion to Machine Learning algorithms. Random Forest algorithm.

    RECOMMENDED READING/BIBLIOGRAPHY

    • Kaminsky, A., BIG CPU, BID DATASolving the World's Toughest Problems with Parallel Computing, CreateSpace Independent Publishing Platform; 1 edition (30 July 2016)
    • Benmammar, B. (2017), Concurrent, Real-Time and Distributed Programming in Java. Newark: John Wiley & Sons, Incorporated.
    • Zaccone, G. (2019), Python Parallel Programming Cookbook, 2nd edition, Packt Publishing. Birmingham, UK.
    • Lecture notes/slides provided by the teacher

    TEACHERS AND EXAM BOARD

    Exam Board

    ERMANNO DI ZITTI (President)

    RICCARDO BERTA (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    Oral examination.

    ASSESSMENT METHODS

    The oral examination will address the mapping of parallel algorithms into parallel architectures for supporting numerical methods. In particular, at the beginning of the exam, each student will have the possibility to present the implementation of an algorithm of his choice into a specific concurrent and distributed architecture, pointing out key points and issues.

    Exam schedule

    Date Time Location Type Notes
    14/01/2022 10:00 GENOVA Esame su appuntamento
    14/01/2022 10:00 GENOVA Orale
    11/02/2022 10:00 GENOVA Esame su appuntamento
    11/02/2022 10:00 GENOVA Orale
    22/07/2022 10:00 GENOVA Esame su appuntamento
    22/07/2022 10:00 GENOVA Orale
    26/08/2022 10:00 GENOVA Esame su appuntamento
    26/08/2022 10:00 GENOVA Orale
    09/09/2022 10:00 GENOVA Esame su appuntamento
    09/09/2022 10:00 GENOVA Orale