Data Flow Machine For Data Driven Computing
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| Author | : John A. Sharp |
| Publisher | : Chichester [West Sussex] : E. Horwood ; New York : Halsted Press |
| Total Pages | : 164 |
| Release | : 1985 |
| Genre | : Computers |
| ISBN | : 9780470201671 |
This book is divided into four parts. Part I discusses the ways in which computations can be modeled. Part II builds on the formal model of computing introduced in the first section, and discusses the implications for programming languages. The implementation of the data flow model of computing, and the various concepts necessary are discussed in Part III. Part IV discusses how the data flow programming languages proposed could be implemented on the new machine architectures suggested in Part III>
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 1995 |
| Genre | : |
| ISBN | : |
A data flow computer which of computing is disclosed which utilizes a data driven processor node architecture. The apparatus in a preferred embodiment includes a plurality of First-In-First-Out (FIFO) registers, a plurality of related data flow memories, and a processor. The processor makes the necessary calculations and includes a control unit to generate signals to enable the appropriate FIFO register receiving the result. In a particular embodiment, there are three FIFO registers per node: an input FIFO register to receive input information form an outside source and provide it to the data flow memories; an output FIFO register to provide output information from the processor to an outside recipient; and an internal FIFO register to provide information from the processor back to the data flow memories. The data flow memories are comprised of four commonly addressed memories. A parameter memory holds the A and B parameters used in the calculations; an opcode memory holds the instruction; a target memory holds the output address; and a tag memory contains status bits for each parameter. One status bit indicates whether the corresponding parameter is in the parameter memory and one status but to indicate whether the stored information in the corresponding data parameter is to be reused. The tag memory outputs a "fire" signal (signal R VALID) when all of the necessary information has been stored in the data flow memories, and thus when the instruction is ready to be fired to the processor.
| Author | : Veljko Milutinovic |
| Publisher | : Springer |
| Total Pages | : 157 |
| Release | : 2017-12-11 |
| Genre | : Computers |
| ISBN | : 3319661256 |
This illuminating text/reference reviews the fundamentals of programming for effective DataFlow computing. The DataFlow paradigm enables considerable increases in speed and reductions in power consumption for supercomputing processes, yet the programming model requires a distinctly different approach. The algorithms and examples showcased in this book will help the reader to develop their understanding of the advantages and unique features of this methodology. This work serves as a companion title to DataFlow Supercomputing Essentials: Research, Development and Education, which analyzes the latest research in this area, and the training resources available. Topics and features: presents an implementation of Neural Networks using the DataFlow paradigm, as an alternative to the traditional ControlFlow approach; discusses a solution to the three-dimensional Poisson equation, using the Fourier method and DataFlow technology; examines how the performance of the Binary Search algorithm can be improved through implementation on a DataFlow architecture; reviews the different way of thinking required to best configure the DataFlow engines for the processing of data in space flowing through the devices; highlights how the DataFlow approach can efficiently support applications in big data analytics, deep learning, and the Internet of Things. This indispensable volume will benefit all researchers interested in 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 an invaluable resource.
| Author | : John A. Sharp |
| Publisher | : Intellect Books |
| Total Pages | : 566 |
| Release | : 1992 |
| Genre | : Computers |
| ISBN | : 9780893919214 |
There is an increasing interest in data flow programming techniques. This interest is motivated in part by the rapid advances in technology (and the need for distributed processing techniques), in part by a desire for faster throughput by applying parallel processing techniques, and in part by search for a programming tool that is closer to the problem solving methods that people naturally adopts rather than current programming languages. This book contains a selection of chapters by researchers on various aspects of the data flow approach in computing. Topics covered include: comparisons of various data flow machine designs, data flow architectures, intentional programming and operator nets, and the relationship between data flow models and modern structured design techniques, among others. The book also includes a brief introduction to the data flow approach, a bibliography, and reviews of where research into data flow might be heading.
| Author | : Jean-Luc Gaudiot |
| Publisher | : |
| Total Pages | : 664 |
| Release | : 1991 |
| Genre | : Computers |
| ISBN | : |
| Author | : Uday Khedker |
| Publisher | : CRC Press |
| Total Pages | : 395 |
| Release | : 2017-12-19 |
| Genre | : Computers |
| ISBN | : 0849332516 |
Data flow analysis is used to discover information for a wide variety of useful applications, ranging from compiler optimizations to software engineering and verification. Modern compilers apply it to produce performance-maximizing code, and software engineers use it to re-engineer or reverse engineer programs and verify the integrity of their programs. Supplementary Online Materials to Strengthen Understanding Unlike most comparable books, many of which are limited to bit vector frameworks and classical constant propagation, Data Flow Analysis: Theory and Practice offers comprehensive coverage of both classical and contemporary data flow analysis. It prepares foundations useful for both researchers and students in the field by standardizing and unifying various existing research, concepts, and notations. It also presents mathematical foundations of data flow analysis and includes study of data flow analysis implantation through use of the GNU Compiler Collection (GCC). Divided into three parts, this unique text combines discussions of inter- and intraprocedural analysis and then describes implementation of a generic data flow analyzer (gdfa) for bit vector frameworks in GCC. Through the inclusion of case studies and examples to reinforce material, this text equips readers with a combination of mutually supportive theory and practice, and they will be able to access the author’s accompanying Web page. Here they can experiment with the analyses described in the book, and can make use of updated features, including: Slides used in the authors’ courses The source of the generic data flow analyzer (gdfa) An errata that features errors as they are discovered Additional updated relevant material discovered in the course of research
| Author | : Lubomir Bic |
| Publisher | : Wiley-IEEE Computer Society Press |
| Total Pages | : 472 |
| Release | : 1995-07-14 |
| Genre | : Computers |
| ISBN | : |
The book includes papers on massively parallel distributed memory and multithreaded architecture design, synchronization and pipelined design, and superpipelined data-driven VLSI processors. Other sections discuss stream data types, the development of well-structured software, and parallelization of dataflow programs.
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 1997 |
| Genre | : |
| ISBN | : |
A data flow computer and method of computing is disclosed which utilizes a data driven processor node architecture. The apparatus in a preferred embodiment includes a plurality of First-In-First-Out (FIFO) registers, a plurality of related data flow memories, and a processor. The processor makes the necessary calculations and includes a control unit to generate signals to enable the appropriate FIFO register receiving the result. In a particular embodiment, there are three FIFO registers per node: an input FIFO register to receive input information form an outside source and provide it to the data flow memories; an output FIFO register to provide output information from the processor to an outside recipient; and an internal FIFO register to provide information from the processor back to the data flow memories. The data flow memories are comprised of four commonly addressed memories. A parameter memory holds the A and B parameters used in the calculations; an opcode memory holds the instruction; a target memory holds the output address; and a tag memory contains status bits for each parameter. One status bit indicates whether the corresponding parameter is in the parameter memory and one status but to indicate whether the stored information in the corresponding data parameter is to be reused. The tag memory outputs a "fire" signal (signal R VALID) when all of the necessary information has been stored in the data flow memories, and thus when the instruction is ready to be fired to the processor.
| Author | : Veljko Milutinović |
| Publisher | : Springer |
| Total Pages | : 136 |
| Release | : 2015-04-28 |
| Genre | : Computers |
| ISBN | : 3319162292 |
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.
| Author | : Sarvesh Pandey |
| Publisher | : Springer Nature |
| Total Pages | : 339 |
| Release | : 2023-01-25 |
| Genre | : Technology & Engineering |
| ISBN | : 3031155424 |
This book discusses the application of data systems and data-driven infrastructure in existing industrial systems in order to optimize workflow, utilize hidden potential, and make existing systems free from vulnerabilities. The book discusses application of data in the health sector, public transportation, the financial institutions, and in battling natural disasters, among others. Topics include real-time applications in the current big data perspective; improving security in IoT devices; data backup techniques for systems; artificial intelligence-based outlier prediction; machine learning in OpenFlow Network; and application of deep learning in blockchain enabled applications. This book is intended for a variety of readers from professional industries, organizations, and students.
