On the Effects of Integrating Region-Based Memory Management and Generational Garbage Collection in ML
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Standard
On the Effects of Integrating Region-Based Memory Management and Generational Garbage Collection in ML. / Elsman, Martin; Hallenberg, Niels.
Practical Aspects of Declarative Languages - 22nd International Symposium, PADL 2020, Proceedings. red. / Ekaterina Komendantskaya; Yanhong Annie Liu. Springer VS, 2020. s. 95-112 (Lecture Notes in Computer Science, Bind 12007 LNCS).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - On the Effects of Integrating Region-Based Memory Management and Generational Garbage Collection in ML
AU - Elsman, Martin
AU - Hallenberg, Niels
N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Generational garbage collection
KW - Region inference
UR - http://www.scopus.com/inward/record.url?scp=85079086586&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39197-3_7
DO - 10.1007/978-3-030-39197-3_7
M3 - Article in proceedings
AN - SCOPUS:85079086586
SN - 9783030391966
T3 - Lecture Notes in Computer Science
SP - 95
EP - 112
BT - Practical Aspects of Declarative Languages - 22nd International Symposium, PADL 2020, Proceedings
A2 - Komendantskaya, Ekaterina
A2 - Liu, Yanhong Annie
PB - Springer VS
T2 - 22nd International Symposium on Practical Aspects of Declarative Languages, PADL 2020
Y2 - 20 January 2020 through 21 January 2020
ER -
ID: 271602700