Emergent Mind

Unifying Linearity and Dependency Analyses

(2304.03175)
Published Apr 6, 2023 in cs.PL

Abstract

Linearity and dependency analyses are key to several applications in computer science, especially, in resource management and information flow control. What connects these analyses is that both of them need to model at least two different worlds with constrained mutual interaction. Though linearity and dependency analyses address similar problems, the analyses are carried out by employing different methods. For linearity analysis, type systems employ the comonadic exponential modality from Girard's linear logic. For dependency analysis, type systems employ the monadic modality from Moggi's computational metalanguage. Owing to this methodical difference, a unification of the two analyses, though theoretically and practically desirable, is not straightforward. Fortunately, with recent advances in graded-context type systems, it has been realized that linearity and dependency analyses can be viewed through the same lens. However, existing graded-context type systems fall short of a unification of linearity and dependency analyses. The problem with existing graded-context type systems is that though their linearity analysis is general, their dependency analysis is limited, primarily because the graded modality they employ is comonadic and not monadic. In this paper, we address this limitation by systematically extending existing graded-context type systems so that the graded modality is both comonadic and monadic. This extension enables us to unify linearity analysis with a general dependency analysis. We present a unified Linear Dependency Calculus, LDC, which analyses linearity and dependency using the same mechanism in an arbitrary Pure Type System. We show that LDC is a general linear and dependency calculus by subsuming into it the standard calculi for the individual analyses.

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