Low-Latency, High-Throughput Garbage Collection (Extended Version)
Wenyu Zhao, Stephen M. Blackburn, Kathryn S. McKinley

TL;DR
LXR is a novel garbage collector that significantly improves responsiveness and throughput by minimizing copying, using reference counting, and introducing a low-overhead write barrier, outperforming existing collectors like Shenandoah.
Contribution
LXR introduces a new approach combining reference counting, limited copying, and a novel write barrier to optimize garbage collection responsiveness and throughput.
Findings
LXR achieves 6x higher throughput on Lucene.
LXR delivers 30x lower tail latency compared to Shenandoah.
LXR effectively balances responsiveness and memory reclamation efficiency.
Abstract
Production garbage collectors make substantial compromises in pursuit of reduced pause times. They require far more CPU cycles and memory than prior simpler collectors. concurrent copying collectors (C4, ZGC, and Shenandoah) suffer from the following design limitations. 1) Concurrent copying. They only reclaim memory by copying, which is inherently expensive with high memory bandwidth demands. Concurrent copying also requires expensive read and write barriers. 2) Scalability. They depend on tracing, which in the limit and in practice does not scale. 3) Immediacy. They do not reclaim older objects promptly, incurring high memory overheads. We present LXR, which takes a very different approach to optimizing responsiveness and throughput by minimizing concurrent collection work and overheads. 1) LXR reclaims most memory without any copying by using the Immix heap structure. It then…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
