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

Title:
AUTOMAP:Inferring Rank-Polymorphic Function Applications with Integer Linear Programming
Source:
Schenck, R, Hinnerskov, N H, Henriksen, T, Madsen, M & Elsman, M 2024, 'AUTOMAP : Inferring Rank-Polymorphic Function Applications with Integer Linear Programming', Proceedings of the ACM on Programming Languages , vol. 8, no. OOPSLA2, 334, pp. 1787-1813. https://doi.org/10.1145/3689774
Publication Year:
2024
Collection:
Aarhus University: Research
Document Type:
*Academic Journal* article in journal/newspaper
Language:
English
ISSN:
2475-1421
Relation:
info:eu-repo/semantics/altIdentifier/pissn/2475-1421; info:eu-repo/semantics/altIdentifier/eissn/2475-1421
DOI:
10.1145/3689774
Rights:
info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by-nc-sa/4.0/
Accession Number:
edsbas.6B85A29E
Database:
BASE

*Further Information*

*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.*