A Parametric Framework for Reversible $\pi$-Calculi

TL;DR

This paper introduces a flexible, parametric framework for reversible pi-calculi that unifies various causal semantics and ensures causally-consistent reversibility in concurrent computations.

Contribution

It proposes a novel, uniform framework parametrized by data structures to model causality in reversible pi-calculi, unifying existing semantics.

Findings

Framework can represent multiple causal semantics

Reversibility is causally consistent within the framework

Establishes causal correspondence with existing semantics

Abstract

This paper presents a study of causality in a reversible, concurrent setting. There exist various notions of causality in pi-calculus, which differ in the treatment of parallel extrusions of the same name. In this paper we present a uniform framework for reversible pi-calculi that is parametric with respect to a data structure that stores information about an extrusion of a name. Different data structures yield different approaches to the parallel extrusion problem. We map three well-known causal semantics into our framework. We show that the (parametric) reversibility induced by our framework is causally- consistent and prove a causal correspondence between an appropriate instance of the framework and Boreale and Sangiorgi's causal semantics.