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Optimizing Layout of Recursive Datatypes with Marmoset: Or, Algorithms {+} Data Layouts {=} Efficient Programs

Authors Vidush Singhal , Chaitanya Koparkar , Joseph Zullo , Artem Pelenitsyn , Michael Vollmer , Mike Rainey , Ryan Newton , Milind Kulkarni

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Author Details

Vidush Singhal
  • Purdue University, West Lafayette, IN, USA
Chaitanya Koparkar
  • Indiana University, Bloomington, IN, USA
Joseph Zullo
  • Purdue University, West Lafayette, IN, USA
Artem Pelenitsyn
  • Purdue University, West Lafayette, IN, USA
Michael Vollmer
  • University of Kent, UK
Mike Rainey
  • Carnegie Mellon University, Pittsburgh, PA, USA
Ryan Newton
  • Purdue University, West Lafayette, IN, USA
Milind Kulkarni
  • Purdue University, West Lafayette, IN, USA

Funding

This work was supported in part by NSF CCF-1908504, CCF-1919197, CCF-2216978, CCF-2119352, CCF-1909862 and EPSRC EP/X021173/1.

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Vidush Singhal, Chaitanya Koparkar, Joseph Zullo, Artem Pelenitsyn, Michael Vollmer, Mike Rainey, Ryan Newton, and Milind Kulkarni. Optimizing Layout of Recursive Datatypes with Marmoset: Or, Algorithms {+} Data Layouts {=} Efficient Programs. In 38th European Conference on Object-Oriented Programming (ECOOP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 313, pp. 38:1-38:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.ECOOP.2024.38

Abstract

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.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Compilers
  • Software and its engineering → Software performance
  • Information systems → Data layout
Keywords
  • Tree traversals
  • Compilers
  • Data layout optimization
  • Dense data layout

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