Exploring the DataFlow Supercomputing Paradigm
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This useful text/reference describes the implementation of a varied selection of algorithms in the DataFlow paradigm, highlighting the exciting potential of DataFlow computing for applications in such areas as image understanding, biomedicine, physics simulation, and business.
The mapping of additional algorithms onto the DataFlow architecture is also covered in the following Springer titles from the same team: DataFlow Supercomputing Essentials: Research, Development and Education, DataFlow Supercomputing Essentials: Algorithms, Applications and Implementations, and Guide to DataFlow…mehr

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Produktbeschreibung
This useful text/reference describes the implementation of a varied selection of algorithms in the DataFlow paradigm, highlighting the exciting potential of DataFlow computing for applications in such areas as image understanding, biomedicine, physics simulation, and business.

The mapping of additional algorithms onto the DataFlow architecture is also covered in the following Springer titles from the same team: DataFlow Supercomputing Essentials: Research, Development and Education, DataFlow Supercomputing Essentials: Algorithms, Applications and Implementations, and Guide to DataFlow Supercomputing.
Topics and Features: introduces a novel method of graph partitioning for large graphs involving the construction of a skeleton graph; describes a cloud-supported web-based integrated development environment that can develop and run programs without DataFlow hardware owned by the user; showcases a new approach for the calculation of the extrema of functions in one dimension, by implementing the Golden Section Search algorithm; reviews algorithms for a DataFlow architecture that uses matrices and vectors as the underlying data structure; presents an algorithm for spherical code design, based on the variable repulsion force method; discusses the implementation of a face recognition application, using the DataFlow paradigm; proposes a method for region of interest-based image segmentation of mammogram images on high-performance reconfigurable DataFlow computers; surveys a diverse range of DataFlow applications in physics simulations, and investigates a DataFlow implementation of a Bitcoin mining algorithm.

This unique volume will prove a valuable reference for researchers and programmers of DataFlow computing, and supercomputing in general. Graduate and advanced undergraduate students will also find that the book serves as an ideal supplementary text for courses on Data Mining, Microprocessor Systems, and VLSISystems.

Autorenporträt
Dr. Veljko Milutinovic teaches DataFlow supercomputing in the School of Informatics, Computing, and Engineering at Indiana University, Bloomington, IN, USA, and previously served for about a decade on the faculty of Purdue University in West Lafayette, IN, USA. He is a co-designer of DARPA's first GaAs RISC microprocessor on 200MHz and a co-designer of the DARPA's 4096-processor systolic array. He is a Life Fellow of the IEEE and a Life Member the ACM. He is a Member of The Academy of Europe, a Member of the Serbian National Academy of Engineering, and a Foreign Member of the Montenegrin Academy of Sciences and Arts. He serves as a Senior Advisor to Maxeler Technologies in London, UK. Mr. Milos Kotlar is a Software Engineer at the Swiss-Swedish company ABB (ASEA Brown Boveri) of Zurich, Switzerland and a Ph.D. student at the School of Electrical Engineering at the University of Belgrade, Serbia. He serves as a TA for DataFlow supercomputing courses and as an RA for DataFlow supercomputing research in the domain of tensor calculus.