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Introductory Overview on Implementation Tools

Chapter
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Part of the Computer Communications and Networks book series (CCN)

Abstract

When programming the dataflow engines, developers need to change the way of thinking from the way of thinking used when programming CPUs where a program that gets executed in time is written to thinking about how to write a spatial recipe that will best configure the dataflow engines so that the data gets processed in space flowing through the configured devices.

References

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    Milutinovic V et al (2015) Guide to dataflow supercomputing: basic concepts, case studies, and a detailed example. Springer, LondonGoogle Scholar
  2. 2.
    Trifunovic N et al (2017) A novel infrastructure for synergistic dataflow research, development, education, and deployment: the Maxeler appgallery project. Advances in computers. ElsevierGoogle Scholar
  3. 3.
    Maxeler Technologies (2016) Maxeler DFE debugging and optimization tutorial, LondonGoogle Scholar
  4. 4.
    Maxeler Technologies, Multiscale dataflow computing the vertical perspective, http://web.stanford.edu/class/ee380/Abstracts/130109-MaxEE380Jan2013.pdf
  5. 5.
    Maxeler Technologies (2017) Multiscale dataflow programming, LondonGoogle Scholar
  6. 6.
    Maxeler Technologies, https://github.com/maxeler/maxskins, visited on 29 June 2017
  7. 7.
    Trifunovic N et al (2017) Cloud deployment and management of dataflow engines. In: ACM, Proceedings of the 1st international workshop on next generation of cloud architectures, BelgradeGoogle Scholar
  8. 8.
    Blagojevic V et al (2016) A systematic approach to generation of new ideas for PhD research in computing. Adv Comput 104:1–19. ElsevierGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Electrical EngineeringUniversity of BelgradeBelgradeSerbia
  2. 2.University of BernBern/Fribourg/NeuchâtelSwitzerland
  3. 3.Maxeler TechnologiesLondonUK

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