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 graphDescribes a cloud-supported web-based integrated development environment that can develop and run programs without DataFlow hardware owned by the userShowcases a new approach forthe calculation of the extrema of functions in one dimension, by implementing the Golden Section Search algorithmReviews algorithms for a DataFlow architecture that uses matrices and vectors as the underlying data structurePresents an algorithm for spherical code design, based on the variable repulsion force methodDiscusses the implementation of a face recognition application, using the DataFlow paradigmProposes a method for region of interest-based image segmentation of mammogram images on high-performance reconfigurable DataFlow computersSurveys 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 VLSI Systems.
