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Learned garbage collection
Name
2004.13301.pdf
Description
Submitted version
Size
363.83 KB
Format
Unknown
Checksum (MD5)
90455db1580e5255d1ae7b8604441496
Author(s)
Cen, Lujing
Marcus, Ryan
Mao, Hongzi
Gottschlich, Justin
Alizadeh, Mohammad
Kraska, Tim
Date Issued
June 2020
Journal
MAPL 2020 - Proceedings of the 4th ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, co-located with PLDI 2020
Publisher
ACM
Version
Original manuscript
Abstract
© 2020 Owner/Author. Several programming languages use garbage collectors (GCs) to automatically manage memory for the programmer. Such collectors must decide when to look for unreachable objects to free, which can have a large performance impact on some applications. In this preliminary work, we propose a design for a learned garbage collector that autonomously learns over time when to perform collections. By using reinforcement learning, our design can incorporate user-defined reward functions, allowing an autonomous garbage collector to learn to optimize the exact metric the user desires (e.g., request latency or queries per second). We conduct an initial experimental study on a prototype, demonstrating that an approach based on tabular Q learning may be promising.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1145/3394450.3397469
