On the Effects of Integrating Region-Based Memory Management and Generational Garbage Collection in ML

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

We present a region-based memory management scheme with support for generational garbage collection. The scheme is implemented in the MLKit Standard ML compiler, which features a compile-time region inference algorithm. The compiler generates native x64 machine code and deploys region types at runtime to avoid write barrier problems and to support partly tag-free garbage collection. We measure the characteristics of the scheme, for a number of benchmarks, and compare it to the Mlton state-of-the-art Standard ML compiler and configurations of the MLKit with and without region inference and generational garbage collection enabled. Although region inference often serves the purpose of generations, we demonstrate that, in some cases, generational garbage collection combined with region inference is beneficial.

Original languageEnglish
Title of host publicationPractical Aspects of Declarative Languages - 22nd International Symposium, PADL 2020, Proceedings
EditorsEkaterina Komendantskaya, Yanhong Annie Liu
Number of pages18
PublisherSpringer VS
Publication date2020
Pages95-112
ISBN (Print)9783030391966
DOIs
Publication statusPublished - 2020
Event22nd International Symposium on Practical Aspects of Declarative Languages, PADL 2020 - New Orleans, United States
Duration: 20 Jan 202021 Jan 2020

Conference

Conference22nd International Symposium on Practical Aspects of Declarative Languages, PADL 2020
LandUnited States
ByNew Orleans
Periode20/01/202021/01/2020
SeriesLecture Notes in Computer Science
Volume12007 LNCS
ISSN0302-9743

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

    Research areas

  • Generational garbage collection, Region inference

ID: 271602700