Data optimizations for constraint automata

TL;DR

This paper introduces two optimization techniques for constraint automata compilers that improve runtime efficiency by reducing redundant variables and translating data constraints into imperative commands, with proven correctness and positive experimental results.

Contribution

The paper presents novel optimization methods for CA-compilers, including variable reduction and data constraint translation, enhancing performance and correctness guarantees.

Findings

Optimizations improve execution speed of CA-based code

Correctness of optimizations is formally proven

Experimental results show performance gains in practice

Abstract

Constraint automata (CA) constitute a coordination model based on finite automata on infinite words. Originally introduced for modeling of coordinators, an interesting new application of CAs is implementing coordinators (i.e., compiling CAs into executable code). Such an approach guarantees correctness-by-construction and can even yield code that outperforms hand-crafted code. The extent to which these two potential advantages materialize depends on the smartness of CA-compilers and the existence of proofs of their correctness. Every transition in a CA is labeled by a "data constraint" that specifies an atomic data-flow between coordinated processes as a first-order formula. At run-time, compiler-generated code must handle data constraints as efficiently as possible. In this paper, we present, and prove the correctness of two optimization techniques for CA-compilers related to handling of data constraints: a reduction to eliminate redundant variables and a translation from (declarative) data constraints to (imperative) data commands expressed in a small sequential language. Through experiments, we show that these optimization techniques can have a positive impact on performance of generated executable code.