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DataFlow Supercomputing Essentials

Research, Development and Education

  • Veljko Milutinovic
  • Jakob Salom
  • Dragan Veljovic
  • Nenad Korolija
  • Dejan Markovic
  • Luka Petrovic

Part of the Computer Communications and Networks book series (CCN)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Research

    1. Front Matter
      Pages 1-1
    2. Veljko Milutinovic, Jakob Salom, Dragan Veljovic, Nenad Korolija, Dejan Markovic, Luka Petrovic
      Pages 3-18
    3. Veljko Milutinovic, Jakob Salom, Dragan Veljovic, Nenad Korolija, Dejan Markovic, Luka Petrovic
      Pages 19-66
  3. Development

    1. Front Matter
      Pages 67-67
    2. Veljko Milutinovic, Jakob Salom, Dragan Veljovic, Nenad Korolija, Dejan Markovic, Luka Petrovic
      Pages 69-105
    3. Veljko Milutinovic, Jakob Salom, Dragan Veljovic, Nenad Korolija, Dejan Markovic, Luka Petrovic
      Pages 107-129
  4. Education

    1. Front Matter
      Pages 131-131
    2. Veljko Milutinovic, Jakob Salom, Dragan Veljovic, Nenad Korolija, Dejan Markovic, Luka Petrovic
      Pages 133-147
  5. Back Matter
    Pages 149-150

About this book

Introduction

This informative text/reference highlights the potential of DataFlow computing in research requiring high speeds, low power requirements, and high precision, while also benefiting from a reduction in the size of the equipment. The cutting-edge research and implementation case studies provided in this book will help the reader to develop their practical understanding of the advantages and unique features of this methodology.

This work serves as a companion title to DataFlow Supercomputing Essentials: Algorithms, Applications and Implementations, which reviews the key algorithms in this area, and provides useful examples.

Topics and features:

  • Reviews the library of tools, applications, and source code available to support DataFlow programming
  • Discusses the enhancements to DataFlow computing yielded by small hardware changes, different compilation techniques, debugging, and optimizing tools
  • Examines when a DataFlow architecture is best applied, and for which types of calculation
  • Describes how converting applications to a DataFlow representation can result in an acceleration in performance, while reducing the power consumption
  • Explains how to implement a DataFlow application on Maxeler hardware architecture, with links to a video tutorial series available online

This enlightening volume will be of great interest to all researchers investigating supercomputing in general, and DataFlow computing in particular. Advanced undergraduate and graduate students involved in courses on Data Mining, Microprocessor Systems, and VLSI Systems, will also find the book to be a helpful reference.​

Keywords

DataFlow Big Data Supercomputing Field-programmable gate array Neural networks

Authors and affiliations

  • Veljko Milutinovic
    • 1
  • Jakob Salom
    • 2
  • Dragan Veljovic
    • 3
  • Nenad Korolija
    • 4
  • Dejan Markovic
    • 5
  • Luka Petrovic
    • 6
  1. 1.University of BelgradeBelgradeSerbia
  2. 2.Serbian Academy of Sciences and ArtsBelgradeSerbia
  3. 3.Motionlogic GmbHBerlinGermany
  4. 4.University of BelgradeBelgradeSerbia
  5. 5.University of BelgradeBelgradeSerbia
  6. 6.University of BelgradeBelgradeSerbia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-66128-5
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-66127-8
  • Online ISBN 978-3-319-66128-5
  • Series Print ISSN 1617-7975
  • Series Online ISSN 2197-8433
  • Buy this book on publisher's site
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