A partial evaluation methodology for optimizing rewrite theories incrementally

被引:0
|
作者
Alpuente, Maria [1 ]
Ballis, Demis [2 ]
Escobar, Santiago [1 ]
Pascual, Daniel Galan [1 ]
Sapina, Julia [1 ]
机构
[1] Univ Politecn Valencia, VRAIN Valencian Res Inst Artificial Intelligence, Camino Vera S-N, Valencia 46020, Spain
[2] Univ Udine, DMIF, Via Sci 206, I-33100 Udine, Italy
基金
欧盟地平线“2020”;
关键词
Concurrent and non-deterministic system modeling; Algebraic specification; Code optimization; Narrowing-based partial evaluation; Symbolic reasoning; Rewriting logic; Maude; LOGIC;
D O I
10.1016/j.mex.2022.101802
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Partial evaluation (PE) is a branch of computer science that achieves code optimization via specialization. This article describes a PE methodology for optimizing rewrite theories that encode concurrent as well as nondeterministic systems by means of the Maude language. The main advantages of the proposed methodology can be summarized as follows: An automatic program optimization technique for rewrite theories featuring several PE criteria that support the specialization of a broad class of rewrite theories. An incremental partial evaluation modality that allows the key specialization components to be encapsulated at the desired granularity level to facilitate progressive refinements of the specialization. All executability theory requirements are preserved by the PE transformation. Also the transformation ensures the semantic equivalence between the original rewrite theory and the specialized theory under rather mild conditions. (C) 2022 The Author(s). Published by Elsevier B.V.
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页数:10
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