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Reversibility and Predictions

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Reversible Computation (RC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12805))

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Abstract

This paper analyses the introduction of a non-reversible mechanism in a reversible calculus (called \(\varOmega \rho \pi \)), intended to be used as an oracle which contains persistent memories of previously reversed computation. As a second step, we introduce the notion of weak causal consistency which relaxes the classical causal consistency by allowing the backward semantics not to revert to a previous state, but to a state related to a previous state and we show that \(\varOmega \rho \pi \) is weakly causally consistent. We finally present a practical application of this calculus.

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Notes

  1. 1.

    Note that, actually, it still has some sort of causal consistency, in that backward semantics undo the latter messages first. Therefore it is not possible to have an effect without its cause, but the resulting state is not reachable without backward sequence.

  2. 2.

    We could generalize this rule by relaxing the constraint that \(Q\precsim P\), by introducing a binary relation of processes \(\mathcal {R}\) as parameter and requiring that \(\langle P, Q\rangle \in \mathcal {R}\), and then instantiating our semantics with \(\precsim \) as \(\mathcal {R}\) in this paper. However, the implications of such generalization are not trivial, in particular with respect to the weak causal consistency result presented latter in this paper. Therefore, for the sake of simplicity, we restrict ourself to the restricted definition.

  3. 3.

    Notice that, due to the pending \(\langle k_5^1, \tilde{k_5}\rangle : c\langle P_1\rangle \) that remains after the choice, if \(k_1\) reduces at this point, when reading \(c\) it could actually receive from this pending process. For the sake of simplicity, we ignore this, since that garbage process is cleaned up when \(k_2\) returns in its initial state.

  4. 4.

    The proof is trivial. Due to length constraints, we omit it.

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Authors and Affiliations

  1. Univ. Grenoble-Alpes, Inria, CNRS, Grenoble Inp, LIG Grenoble, Grenoble, France

    Martin Vassor

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  1. Martin Vassor
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Correspondence to Martin Vassor .

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Editors and Affiliations

  1. Ritsumeikan University, Kusatsu, Japan

    Shigeru Yamashita

  2. Nanzan University, Nagoya, Japan

    Tetsuo Yokoyama

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Vassor, M. (2021). Reversibility and Predictions. In: Yamashita, S., Yokoyama, T. (eds) Reversible Computation. RC 2021. Lecture Notes in Computer Science(), vol 12805. Springer, Cham. https://doi.org/10.1007/978-3-030-79837-6_10

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  • DOI: https://doi.org/10.1007/978-3-030-79837-6_10

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