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Superlinear Scalability in Parallel Computing and Multi-robot Systems: Shared Resources, Collaboration, and Network Topology

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Architecture of Computing Systems – ARCS 2018 (ARCS 2018)

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

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Abstract

The uniting idea of both parallel computing and multi-robot systems is that having multiple processors or robots working on a task decreases the processing time. Typically we desire a linear speedup, that is, doubling the number of processing units halves the execution time. Sometimes superlinear scalability is observed in parallel computing systems and more frequently in multi-robot and swarm systems. Superlinearity means each individual processing unit gets more efficient by increasing the system size—a desired and rather counterintuitive phenomenon.

In an interdisciplinary approach, we compare abstract models of system performance from three different fields of research: parallel computing, multi-robot systems, and network science. We find agreement in the modeled universal properties of scalability and summarize our findings by formulating more generic interpretations of the observed phenomena. Our result is that scalability across fields can be interpreted as a tradeoff in three dimensions between too competitive and too cooperative processing schemes, too little information sharing and too much information sharing, while finding a balance between neither underusing nor depleting shared resources. We successfully verify our claims by two simple simulations of a multi-robot and a network system.

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Authors and Affiliations

  1. Department of Computer Engineering, University of Lübeck, Lübeck, Germany

    Heiko Hamann

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  1. Heiko Hamann
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Correspondence to Heiko Hamann .

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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Hamann, H. (2018). Superlinear Scalability in Parallel Computing and Multi-robot Systems: Shared Resources, Collaboration, and Network Topology. 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_3

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  • DOI: https://doi.org/10.1007/978-3-319-77610-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77609-5

  • Online ISBN: 978-3-319-77610-1

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