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Communication Dans Un Congrès Année : 2024

Optimizing a Non-Deterministic Abstract Machine with Environments

Résumé

Non-deterministic abstract machine (NDAM) is a recent implementation model for programming languages where one must choose among several redexes at each reduction step, like process calculi. These machines can be derived from a zipper semantics, a mix between structural operational semantics and context-based reduction semantics. Such a machine has been generated also for the λ-calculus without a fixed reduction strategy, i.e., with the full non-deterministic β-reduction. In that machine, substitution is an external operation that replaces all the occurrences of a variable at once. Implementing substitution with environments is more low-level and more efficient as variables are replaced only when needed. In this paper, we define a NDAM with environments for the λ-calculus without a fixed reduction strategy. We also introduce other optimizations, including a form of refocusing, and we show that we can restrict our optimized NDAM to recover some of the usual λ-calculus machines, e.g., the Krivine Abstract Machine. Most of the improvements we propose in this work could be applied to other NDAMs as well.
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hal-04643294 , version 1 (10-07-2024)

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Małgorzata Biernacka, Dariusz Biernacki, Sergueï Lenglet, Alan Schmitt. Optimizing a Non-Deterministic Abstract Machine with Environments. FSCD 2024 - 9th International Conference on Formal Structures for Computation and Deduction, Jul 2024, Tallinn, Estonia. pp.1-22, ⟨10.4230/LIPIcs.FSCD.2024.11⟩. ⟨hal-04643294⟩
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