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Titlebook: DataFlow Supercomputing Essentials; Algorithms, Applicat Veljko Milutinovic,Milos Kotlar,Zoran Babovic Book 2017 Springer International Pub

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書目名稱DataFlow Supercomputing Essentials
副標(biāo)題Algorithms, Applicat
編輯Veljko Milutinovic,Milos Kotlar,Zoran Babovic
視頻videohttp://file.papertrans.cn/264/263341/263341.mp4
概述Reviews the advantages of the DataFlow paradigm for supercomputing.Introduces the DataFlow programming model.Provides a selection of algorithm examples that illuminate the DataFlow paradigm
叢書名稱Computer Communications and Networks
圖書封面Titlebook: DataFlow Supercomputing Essentials; Algorithms, Applicat Veljko Milutinovic,Milos Kotlar,Zoran Babovic Book 2017 Springer International Pub
描述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 throug
出版日期Book 2017
關(guān)鍵詞DataFlow; Big Data; Supercomputing; Field-programmable gate array; Neural networks
版次1
doihttps://doi.org/10.1007/978-3-319-66125-4
isbn_softcover978-3-319-88183-6
isbn_ebook978-3-319-66125-4Series ISSN 1617-7975 Series E-ISSN 2197-8433
issn_series 1617-7975
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Binary Search in the DataFlow Paradigm. In the chapter you will see the Binary Search algorithm explained and the differences between its implementations on two different architectures. It will be shown that the difference in the amount of data needed to be processed is in connection with the resulting speedup achieved on a Maxeler mach
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DataFlow Systems: From Their Origins to Future Applications in Data Analytics, Deep Learning, and thdomain-specific hardware systems which empower big data and high-performance applications. Likewise, dataflow systems are experiencing a revival with both hardware and software approaches widely exploited. In our work, we give an overview of dataflow system origins and similar technologies such as s
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