A Parametric Framework for Reversible Pi-Calculi

TL;DR

This paper introduces a flexible, parametric framework for reversible pi-calculi that unifies various causality notions and ensures causally-consistent reversibility, linking to established causal semantics.

Contribution

It proposes a novel, parametric framework for reversible pi-calculi that can represent different causal semantics and establish formal causal correspondences.

Findings

Framework is causally-consistent

Maps three causal semantics into 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.