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International Conference on Architecture of Computing Systems

ARCS 2018: Architecture of Computing Systems – ARCS 2018 pp 297–310Cite as

Do Iterative Solvers Benefit from Approximate Computing? An Evaluation Study Considering Orthogonal Approximation Methods

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10793))

Abstract

Employing algorithms of scientific computing often comes in hand with finding a trade-off between accuracy and performance. Novel parallel hardware and algorithms only slightly improve these issues due to the increasing size of the problems. While high accuracy is inevitable for most problems, there are parts in scientific computing that allow us to introduce approximation. Therefore, in this paper we give answers to the following questions: (1) Can we exploit different approximate computing strategies in scientific computing? (2) Is there a strategy to combine approaches? To answer these questions, we apply different approximation strategies to a widely used iterative solver for linear systems of equations. We show the advantages and the limits of each strategy and a way to configure a combination of strategies according to a given relative error. Combining orthogonal strategies as an overall concept gives us significant opportunities to increase the performance.

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Author information

Authors and Affiliations

  1. Computer Architecture and Parallel Processing, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Michael Bromberger & Markus Hoffmann

  2. Database Technology Group, Technische Universität Dresden, Dresden, Germany

    Robin Rehrmann

Authors
  1. Michael Bromberger
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  2. Markus Hoffmann
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  3. Robin Rehrmann
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Corresponding author

Correspondence to Michael Bromberger .

Editor information

Editors and Affiliations

  1. Chair for Chip Design for Embedded Computing, Technische Universität Braunschweig, Braunschweig, Germany

    Mladen Berekovic

  2. Chair for Chip Design for Embedded Computing, Technische Universität Braunschweig, Braunschweig, Germany

    Rainer Buchty

  3. Institute of Computer Engineering, Universität zu Lübeck, Lübeck, Germany

    Heiko Hamann

  4. School of Computer Science, The University of Manchester, Manchester, United Kingdom

    Dirk Koch

  5. Institute for Information Technology and Communications, Otto-von-Guericke Universität Magdeburg, Magdeburg, Germany

    Thilo Pionteck

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Bromberger, M., Hoffmann, M., Rehrmann, R. (2018). Do Iterative Solvers Benefit from Approximate Computing? An Evaluation Study Considering Orthogonal Approximation Methods. In: Berekovic, M., Buchty, R., Hamann, H., Koch, D., Pionteck, T. (eds) Architecture of Computing Systems – ARCS 2018. ARCS 2018. Lecture Notes in Computer Science(), vol 10793. Springer, Cham. https://doi.org/10.1007/978-3-319-77610-1_22

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  • DOI: https://doi.org/10.1007/978-3-319-77610-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77609-5

  • Online ISBN: 978-3-319-77610-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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