This unique text/reference describes an exciting and novel approach to supercomputing in the Data Flow 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 Data Flow supercomputing for big data problems; reviews the latest research on the Data Flow 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 Data Flow machine; includes a step-by-step guide to the web-based integrated development environment Web IDE.
Innehållsförteckning
The Data Flow Paradigm.- Selected Case Studies.- An Example Application: Fourier Transform.- Using the Web IDE.
Om författaren
Veljko Milutinović 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 Data Flow-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”.
Jakob Salom is a member of the Mathematical Institute of the Serbian Academy of Sciences and Arts. He has delivered numerous Data Flow courses at a number of European universities.
Nemanja Trifunovic is a Project Manager at Maxeler Technologies, Palo Alto, CA, USA. He is the author of a number of Maxeler-based Data Flow implementations and related tools.
Roberto Giorgi is an Associate Professor of Computer Engineering at the University of Siena, Italy. He led TERAFLUX, the largest EU funded research project on Data Flow technologies.
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