Overview
- Reviews the advantages of the DataFlow paradigm for supercomputing
- Introduces the DataFlow programming model
- Provides a selection of algorithm examples that illuminate the DataFlow paradigm
Part of the book series: Computer Communications and Networks (CCN)
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Table of contents (5 chapters)
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Algorithms
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Applications
Keywords
About this book
This work serves as a companion title to DataFlow Supercomputing Essentials: Research, Development and Education, which analyzes the latest research in this area, and the training resources available.
Topics and features: presents an implementation of Neural Networks using the DataFlow paradigm, as an alternative to the traditional ControlFlow approach; discusses a solution to the three-dimensional Poisson equation, using the Fourier method and DataFlow technology; examines how the performance of the Binary Search algorithm can be improved through implementation on a DataFlow architecture; reviews the different way of thinking required to best configure the DataFlow engines for the processing of data in space flowing through the devices; highlights how the DataFlow approach can efficiently support applications in big data analytics, deep learning, and the Internet of Things.This indispensable volume will benefit all researchers interested in 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 an invaluable resource.
Authors and Affiliations
Bibliographic Information
Book Title: DataFlow Supercomputing Essentials
Book Subtitle: Algorithms, Applications and Implementations
Authors: Veljko Milutinovic, Milos Kotlar, Marko Stojanovic, Igor Dundic, Nemanja Trifunovic, Zoran Babovic
Series Title: Computer Communications and Networks
DOI: https://doi.org/10.1007/978-3-319-66125-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-66124-7Published: 22 December 2017
Softcover ISBN: 978-3-319-88183-6Published: 04 September 2018
eBook ISBN: 978-3-319-66125-4Published: 11 December 2017
Series ISSN: 1617-7975
Series E-ISSN: 2197-8433
Edition Number: 1
Number of Pages: XI, 150
Number of Illustrations: 2 b/w illustrations, 50 illustrations in colour
Topics: Operating Systems, System Performance and Evaluation, Computer Engineering, Big Data