About this book
This unique text/reference describes an exciting and novel approach to supercomputing in the DataFlow paradigm. The major advantages and applications of this approach are clearly described, and a detailed explanation of the programming model is provided using simple yet effective examples. The work is developed from a series of lecture courses taught by the authors in more than 40 universities across more than 20 countries, and from research carried out by Maxeler Technologies, Inc.
Topics and features:
- Presents a thorough introduction to DataFlow supercomputing for big data problems
- Reviews the latest research on the DataFlow architecture and its applications
- Introduces a new method for the rapid handling of real-world challenges involving large datasets
- Provides a case study on the use of the new approach to accelerate the Cooley-Tukey algorithm on a DataFlow machine
- Includes a step-by-step guide to the web-based integrated development environment WebIDE
- Draws from the authors’ extensive experience in both academic teaching and industrial research
Students, lecturers, and researchers in industry will find this concise book to be an ideal supplementary text for courses and seminars on VLSI, multi-core systems, and DataFlow computing.
Dr. Veljko Milutinović is a Professor in the Department of Computer Engineering at the University of Belgrade, Serbia. His publications include the Springer title Application and Multidisciplinary Aspects of Wireless Sensor Networks. Dr. Jakob Salom is a member of the Mathematical Institute of the Serbian Academy of Sciences and Arts. Nemanja Trifunovic is a Project Manager at Maxeler Technologies, Palo Alto, CA, USA. Dr. Roberto Giorgi is an Associate Professor of Computer Engineering at the University of Siena, Italy.
- DOI https://doi.org/10.1007/978-3-319-16229-4
- Copyright Information Springer International Publishing Switzerland 2015
- Publisher Name Springer, Cham
- eBook Packages Computer Science Computer Science (R0)
- Print ISBN 978-3-319-16228-7
- Online ISBN 978-3-319-16229-4
- Series Print ISSN 1617-7975
- Series Online ISSN 2197-8433
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