Guide to DataFlow Supercomputing - Milutinovic, Veljko;Salom, Jakob;Trifunovic, Nemanja
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
Veljko Milutinovic is a Professor in the Department of Computer Engineering at the University of Belgrade, Serbia. He is a member of the Scientific Advisory Board of Maxeler Technologies, and a coauthor of two seminal DataFlow-oriented papers: "Moving from Petaflops (on Simple Benchmarks) to Petadata per Unit of Time and Power (on Sophisticated Benchmarks)" and "FPGA Accelerator for Floating-Point Matrix Multiplication".