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 VLSI Systems.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

This book explores the challenges of managing software projects, such as changing requirements, uncertain technologies, and evolving user needs, provides strategies for addressing these and other emerging issues, and contains a number of eye-opening perspectives from experts in different fields. Instead of relying solely on traditional project management techniques, the book presents a holistic, adaptive, and flexible framework that takes into account the unique challenges of each particular case of software development. It recognizes that software development is a complex and creative process that involves people with diverse skills and personalities, and provides insights into how to motivate and manage teams, how to communicate effectively, how to automate processes, and how to deal with conflict and uncertainty, from computer engineering and mathematical logic, all the way to advanced geophysics and earthquake engineering. It provides a wealth of practical advice and guidance, as well as insights into the latest schools of thought related to software project management.

A systematic overview of the quickly developing field of bioengineering—with state-of-the-art modeling software! Computational Modeling and Simulation Examples in Bioengineering provides a comprehensive introduction to the emerging field of bioengineering. It provides the theoretical background necessary to simulating pathological conditions in the bones, muscles, cardiovascular tissue, and cancers, as well as lung and vertigo disease. The methodological approaches used for simulations include the finite element, dissipative particle dynamics, and lattice Boltzman. The text includes access to a state-of-the-art software package for simulating the theoretical problems. In this way, the book enhances the reader's learning capabilities in the field of biomedical engineering. The aim of this book is to provide concrete examples of applied modeling in biomedical engineering. Examples in a wide range of areas equip the reader with a foundation of knowledge regarding which problems can be modeled with which numerical methods. With more practical examples and more online software support than any competing text, this book organizes the field of computational bioengineering into an accessible and thorough introduction. Computational Modeling and Simulation Examples in Bioengineering: Includes a state-of-the-art software package enabling readers to engage in hands-on modeling of the examples in the book Provides a background on continuum and discrete modeling, along with equations and derivations for three key numerical methods Considers examples in the modeling of bones, skeletal muscles, cartilage, tissue engineering, blood flow, plaque, and more Explores stent deployment modeling as well as stent design and optimization techniques Generates different examples of fracture fixation with respect to the advantages in medical practice applications Computational Modeling and Simulation Examples in Bioengineering is an excellent textbook for students of bioengineering, as well as a support for basic and clinical research. Medical doctors and other clinical professionals will also benefit from this resource and guide to the latest modeling techniques.

This book constitutes the refereed proceedings of the 12th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2022, held in Helsinki, Finland, in June 2022. The 13 full papers presented were carefully reviewed and selected from 21 submissions. The papers address various topics such as information and knowledge systems, including submissions that apply ideas, theories or methods from specific disciplines to information and knowledge systems. Examples of such disciplines are discrete mathematics, logic and algebra, model theory, databases, information theory, complexity theory, algorithmics and computation, statistics and optimization.

Creativity in Computing and DataFlow Supercomputing, the latest release in the Advances in Computers series published since 1960, presents detailed coverage of innovations in computer hardware, software, theory, design, and applications. In addition, it provides contributors with a medium in which they can explore topics in greater depth and breadth than journal articles typically allow. As a result, many articles have become standard references that continue to be of significant, lasting value in this rapidly expanding field. - Provides in-depth surveys and tutorials on new computer technology - Presents well-known authors and researchers in the field - Includes extensive bibliographies with most chapters - Contains extensive chapter coverage that is devoted to single themes or subfields of computer science

This informative text/reference highlights the potential of DataFlow computing in research requiring high speeds, low power requirements, and high precision, while also benefiting from a reduction in the size of the equipment. The cutting-edge research and implementation case studies provided in this book will help the reader to develop their practical understanding of the advantages and unique features of this methodology. This work serves as a companion title to DataFlow Supercomputing Essentials: Algorithms, Applications and Implementations, which reviews the key algorithms in this area, and provides useful examples. Topics and features: reviews the library of tools, applications, and source code available to support DataFlow programming; discusses the enhancements to DataFlow computing yielded by small hardware changes, different compilation techniques, debugging, and optimizing tools; examines when a DataFlow architecture is best applied, and for which types of calculation; describes how converting applications to a DataFlow representation can result in an acceleration in performance, while reducing the power consumption; explains how to implement a DataFlow application on Maxeler hardware architecture, with links to a video tutorial series available online. This enlightening volume will be of great interest to all researchers investigating 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 a helpful reference.

As computers continue to remain essential tools for the pursuit of physics, medicine, economics, social sciences, and more, supercomputers are proving that they can further extend and greatly enhance as-of-yet undiscovered knowledge and solve the world’s most complex problems. As these instruments continue to lead to groundbreaking discoveries and breakthroughs, it is imperative that research remains up to date with the latest findings and uses. The Handbook of Research on Methodologies and Applications of Supercomputing is a comprehensive and critical reference book that provides research on the latest advances of control flow and dataflow supercomputing and highlights selected emerging big data applications needing high acceleration and/or low power. Consequently, this book advocates the need for hybrid computing, where the control flow part represents the host architecture and dataflow part represents the acceleration architecture. These issues cover the initial eight chapters. The remaining eight chapters cover selected modern applications that are best implemented on a hybrid computer, in which the transactional parts (serial code) are implemented on the control flow part and the loops (parallel code) on the dataflow part. These final eight chapters cover two major application domains: scientific computing and computing for digital economy. This book offers applications in marketing, medicine, energy systems, and library science, among others, and is an essential source for scientists, programmers, engineers, practitioners, researchers, academicians, and students interested in the latest findings and advancements in supercomputing.

"This book offers a variety of perspectives and summarize the advances of control flow and data flow super computing, shedding light on selected emerging big data applications needing high acceleration and/or low power"--