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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.
Programming --- Computer architecture. Operating systems --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- datamining --- computers --- datacommunicatie --- programmeren (informatica) --- software engineering --- architectuur (informatica) --- data acquisition
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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.
Computer architecture. Operating systems --- Information systems --- Computer. Automation --- IoT (Internet of Things) --- big data --- deep learning --- text mining --- informatica --- computerbesturingssystemen --- gegevensanalyse --- OS (operating system)
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