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Design and Implementation of Highly Scalable Quantifiable Data Structures

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Parallel Computing Technologies (PaCT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12942))

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Abstract

Architectural imperatives due to the slowing of Moore’s Law, the broad acceptance of relaxed semantics and the O(n!) worst case verification complexity of generating sequential histories motivate a new approach to concurrent correctness. Quantifiability is proposed as a novel correctness condition that models a system in vector space to launch a new mathematical analysis of concurrency. Analysis is facilitated with linear algebra, better supported and of much more efficient time complexity than traditional combinatorial methods. In this paper, we design and implement a quantifiable stack (QStack) and queue (QQueue) and present results showing that quantifiable data structures are highly scalable through use of relaxed semantics, an explicit implementation trade-off permitted by quantifiability. We present a technique for proving that a data structure is quantifiable and apply this technique to show that the QStack is quantifiably correct.

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Authors and Affiliations

  1. University of Central Florida, Orlando, FL, 32816, USA

    Victor Cook, Christina Peterson, Zachary Painter & Damian Dechev

Authors
  1. Victor Cook
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  2. Christina Peterson
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  3. Zachary Painter
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  4. Damian Dechev
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Corresponding author

Correspondence to Christina Peterson .

Editor information

Editors and Affiliations

  1. Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russia

    Victor Malyshkin

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Cook, V., Peterson, C., Painter, Z., Dechev, D. (2021). Design and Implementation of Highly Scalable Quantifiable Data Structures. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2021. Lecture Notes in Computer Science(), vol 12942. Springer, Cham. https://doi.org/10.1007/978-3-030-86359-3_28

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  • DOI: https://doi.org/10.1007/978-3-030-86359-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86358-6

  • Online ISBN: 978-3-030-86359-3

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