Titlebook: Exploring the DataFlow Supercomputing Paradigm; Example Algorithms f Veljko Milutinovic,Milos Kotlar Book 2019 Springer Nature Switzerland

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Application of Maxeler DataFlow Supercomputing to Spherical Code Design number of operations it performs make it suitable for implementation on dataflow supercomputing devices. Gains in computation speed and power consumption?of such an implementation are given. Achieved minimum distances?and simulated error probabilities of obtained codes are presented.

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978-3-030-13805-9Springer Nature Switzerland AG 2019

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Exploring the DataFlow Supercomputing Paradigm978-3-030-13803-5Series ISSN 1617-7975 Series E-ISSN 2197-8433

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An Overview of Selected DataFlow Applications in Physics SimulationsThis chapter presents a wide spectrum of the dataflow implementations of applications in physics simulations. All the examples are uniformly presented, which makes possible an easy design and performance comparison among the presented dataflow algorithms.

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https://doi.org/10.1007/978-3-031-27848-8der. Each instruction is fetched from the memory before it could be executed. Passing the data from the instruction that produces it to the instruction that requires it is done using registers or memory. DataFlow computer architectures are configured for execution of an algorithm, while data travel

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Intestinal Bacteria and Disease,ow algorithms usually run as an optimized part of a classical control-flow procedure, which includes reading, writing, and other manipulation of data in such a way that it is easily processed by the dataflow part. Such data handling is usually referred to as data choreography and, in order to obtain

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