Overview
- Addresses theoretical issues in the implementation of the DataFlow model
- Examines a range of mathematical applications that benefit from the use of the DataFlow paradigm
- Presents a varied selection of examples that use DataFlow computing in applications for image understanding, biomedicine, physics simulation, and business
Part of the book series: Computer Communications and Networks (CCN)
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Table of contents (9 chapters)
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Front Matter
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Applications in Image Understanding, Biomedicine, Physics Simulation, and Business
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Front Matter
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Back Matter
Keywords
About this book
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 VLSISystems.Editors and Affiliations
About the editors
Mr. Milos Kotlar is a Software Engineer at the Swiss-Swedish company ABB (ASEA Brown Boveri) of Zurich, Switzerland and a Ph.D. student at the School of Electrical Engineering at the University of Belgrade, Serbia. He serves as a TA for DataFlow supercomputing courses and as an RA for DataFlow supercomputing research in the domain of tensor calculus.
Bibliographic Information
Book Title: Exploring the DataFlow Supercomputing Paradigm
Book Subtitle: Example Algorithms for Selected Applications
Editors: Veljko Milutinovic, Milos Kotlar
Series Title: Computer Communications and Networks
DOI: https://doi.org/10.1007/978-3-030-13803-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-13802-8Published: 13 June 2019
Softcover ISBN: 978-3-030-13805-9Published: 15 August 2020
eBook ISBN: 978-3-030-13803-5Published: 27 May 2019
Series ISSN: 1617-7975
Series E-ISSN: 2197-8433
Edition Number: 1
Number of Pages: X, 315
Number of Illustrations: 111 b/w illustrations, 101 illustrations in colour
Topics: Computer Communication Networks, Communications Engineering, Networks, Big Data, Computation by Abstract Devices, Input/Output and Data Communications