On the Effects of Integrating Region-Based Memory Management and Generational Garbage Collection in ML
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-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 language | English |
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Title of host publication | Practical Aspects of Declarative Languages - 22nd International Symposium, PADL 2020, Proceedings |
Editors | Ekaterina Komendantskaya, Yanhong Annie Liu |
Number of pages | 18 |
Publisher | Springer VS |
Publication date | 2020 |
Pages | 95-112 |
ISBN (Print) | 9783030391966 |
DOIs | |
Publication status | Published - 2020 |
Event | 22nd International Symposium on Practical Aspects of Declarative Languages, PADL 2020 - New Orleans, United States Duration: 20 Jan 2020 → 21 Jan 2020 |
Conference
Conference | 22nd International Symposium on Practical Aspects of Declarative Languages, PADL 2020 |
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Land | United States |
By | New Orleans |
Periode | 20/01/2020 → 21/01/2020 |
Series | Lecture Notes in Computer Science |
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Volume | 12007 LNCS |
ISSN | 0302-9743 |
Bibliographical note
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
- Generational garbage collection, Region inference
Research areas
ID: 271602700