2026-07-26T01:05:15Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:20887 2024-10-15T18:18:49Z ddc:004 open_access

Optimizing Layout of Recursive Datatypes with Marmoset: Or, Algorithms + Data Layouts = Efficient Programs Singhal, Vidush Koparkar, Chaitanya Zullo, Joseph Pelenitsyn, Artem Vollmer, Michael Rainey, Mike Newton, Ryan Kulkarni, Milind Tree traversals Compilers Data layout optimization Dense data layout While programmers know that memory representation of data structures can have significant effects on performance, compiler support to optimize the layout of those structures is an under-explored field. Prior work has optimized the layout of individual, non-recursive structures without considering how collections of those objects in linked or recursive data structures are laid out. This work introduces Marmoset, a compiler that optimizes the layouts of algebraic datatypes, with a special focus on producing highly optimized, packed data layouts where recursive structures can be traversed with minimal pointer chasing. Marmoset performs an analysis of how a recursive ADT is used across functions to choose a global layout that promotes simple, strided access for that ADT in memory. It does so by building and solving a constraint system to minimize an abstract cost model, yielding a predicted efficient layout for the ADT. Marmoset then builds on top of Gibbon, a prior compiler for packed, mostly-serial representations, to synthesize optimized ADTs. We show experimentally that Marmoset is able to choose optimal layouts across a series of microbenchmarks and case studies, outperforming both Gibbon’s baseline approach, as well as MLton, a Standard ML compiler that uses traditional pointer-heavy representations. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Vidush Singhal and Chaitanya Koparkar and Joseph Zullo and Artem Pelenitsyn and Michael Vollmer and Mike Rainey and Ryan Newton and Milind Kulkarni 2024 Is Part Of LIPIcs, Volume 313, 38th European Conference on Object-Oriented Programming (ECOOP 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ECOOP.2024.38 urn:nbn:de:0030-drops-208875 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2024.38 eng https://creativecommons.org/licenses/by/4.0/legalcode