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Exploring the DataFlow Supercomputing Paradigm

Example Algorithms for Selected Applications

  • Veljko Milutinovic
  • Milos Kotlar
Book
  • 1.3k Downloads

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

Table of contents

  1. Front Matter
    Pages i-x
  2. Theoretical Issues

  3. Applications in Mathematics

    1. Front Matter
      Pages 53-53
    2. Ivan Stanojević, Mladen Kovačević, Vojin Šenk
      Pages 133-168
  4. Applications in Image Understanding, Biomedicine, Physics Simulation, and Business

    1. Front Matter
      Pages 169-169
    2. Tijana Sustersic, Aleksandra Vulovic, Nemanja Trifunovic, Ivan Milankovic, Nenad Filipovic
      Pages 171-196
    3. Aleksandar S. Peulic, Ivan Milankovic, Nikola V. Mijailovic, Nenad Filipovic
      Pages 197-227
    4. Rok Meden, Anton Kos
      Pages 241-311
  5. Back Matter
    Pages 313-315

About this book

Introduction

This useful text/reference describes the implementation of a varied selection of algorithms in the DataFlow paradigm, highlighting the exciting potential of DataFlow computing for applications in such areas as image understanding, biomedicine, physics simulation, and business.

The mapping of additional algorithms onto the DataFlow architecture is also covered in the following Springer titles from the same team: DataFlow Supercomputing Essentials: Research, Development and Education, DataFlow Supercomputing Essentials: Algorithms, Applications and Implementations, and Guide to DataFlow Supercomputing.

Topics and Features:

  • Introduces a novel method of graph partitioning for large graphs involving the construction of a skeleton graph
  • Describes a cloud-supported web-based integrated development environment that can develop and run programs without DataFlow hardware owned by the user
  • Showcases a new approach for the calculation of the extrema of functions in one dimension, by implementing the Golden Section Search algorithm
  • Reviews algorithms for a DataFlow architecture that uses matrices and vectors as the underlying data structure
  • Presents an algorithm for spherical code design, based on the variable repulsion force method
  • Discusses the implementation of a face recognition application, using the DataFlow paradigm
  • Proposes a method for region of interest-based image segmentation of mammogram images on high-performance reconfigurable DataFlow computers
  • Surveys a diverse range of DataFlow applications in physics simulations, and investigates a DataFlow implementation of a Bitcoin mining algorithm

This unique volume will prove a valuable reference for researchers and programmers of DataFlow computing, and supercomputing in general. Graduate and advanced undergraduate students will also find that the book serves as an ideal supplementary text for courses on Data Mining, Microprocessor Systems, and VLSI Systems.

Keywords

Big data DataFlow Supercomputing FPGA Performance evaluation

Editors and affiliations

  • Veljko Milutinovic
    • 1
  • Milos Kotlar
    • 2
  1. 1.Indiana UniversityBloomingtonUSA
  2. 2.School of Electrical EngineeringUniversity of BelgradeBelgradeSerbia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-13803-5
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-13802-8
  • Online ISBN 978-3-030-13803-5
  • Series Print ISSN 1617-7975
  • Series Online ISSN 2197-8433
  • Buy this book on publisher's site