Work

AUTOMAP: Inferring Rank-Polymorphic Function Applications with Integer Linear Programming

Year: 2024

Type: article

Abstract: Dynamically typed array languages such as Python, APL, and Matlab lift scalar operations to arrays and replicate scalars to fit applications. We present a mechanism for automatically inferring map and replicate operations in a statically-typed language in a way that resembles the programming experience of a dynamically-typed language while preserving the static typing guarantees. Our type system—which supports parametric polymorphism, higher-order functions, and top-level let-generalization—makes use of integer linear programming in order to find the minimum number of operations needed to elaborate to a well-typed program. We argue that the inference system provides useful and unsurprising guarantees to the programmer. We demonstrate important theoretical properties of the mechanism and report on the implementation of the mechanism in the statically-typed array programming language Futhark.

Source: Proceedings of the ACM on Programming Languages

DOI: 10.1145/3689774

Authors: Robert Schenck, Nikolaj Hey Hinnerskov, Troels Henriksen, Magnus Madsen, Martin Elsman

Institutions: University of Copenhagen, Aarhus University

Language: English

Topic: Logic, programming, and type systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4403223284

FWCI: 0.147

Cites: 37

Cited by: 1

Related to: 10