DataFlow Supercomputing Essentials

Algorithms, Applications and Implementations

Nonfiction, Computers, Computer Hardware, Operating Systems, General Computing
Cover of the book DataFlow Supercomputing Essentials by Zoran Babovic, Igor Dundic, Veljko Milutinovic, Nemanja Trifunovic, Milos Kotlar, Marko Stojanovic, Springer International Publishing
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Author: Zoran Babovic, Igor Dundic, Veljko Milutinovic, Nemanja Trifunovic, Milos Kotlar, Marko Stojanovic ISBN: 9783319661254
Publisher: Springer International Publishing Publication: December 11, 2017
Imprint: Springer Language: English
Author: Zoran Babovic, Igor Dundic, Veljko Milutinovic, Nemanja Trifunovic, Milos Kotlar, Marko Stojanovic
ISBN: 9783319661254
Publisher: Springer International Publishing
Publication: December 11, 2017
Imprint: Springer
Language: English

This illuminating text/reference reviews the fundamentals of programming for effective DataFlow computing. The DataFlow paradigm enables considerable increases in speed and reductions in power consumption for supercomputing processes, yet the programming model requires a distinctly different approach. The algorithms and examples showcased in this book will help the reader to develop their understanding of the advantages and unique features of this methodology.

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This illuminating text/reference reviews the fundamentals of programming for effective DataFlow computing. The DataFlow paradigm enables considerable increases in speed and reductions in power consumption for supercomputing processes, yet the programming model requires a distinctly different approach. The algorithms and examples showcased in this book will help the reader to develop their understanding of the advantages and unique features of this methodology.

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.

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