MemLiner: Lining up Tracing and Application for a Far-Memory-Friendly Runtime


Chenxi Wang, Haoran Ma, Shi Liu, Yifan Qiao, Jonathan Eyolfson, and Christian Navasca, UCLA; Shan Lu, University of Chicago; Guoqing Harry Xu, UCLA

Awarded Best Paper!


Far-memory techniques that enable applications to use remote memory are increasingly appealing in modern datacenters, supporting applications’ large memory footprint and improving machines’ resource utilization. Unfortunately, most far-memory techniques focus on OS-level optimizations and are agnostic to managed runtimes and garbage collections (GC) underneath applications written in high-level languages. With different object-access patterns from applications, GC can severely interfere with existing far-memory techniques, breaking prefetching algorithms and causing severe local-memory misses.

We developed MemLiner, a runtime technique that improves the performance of far-memory systems by “lining up” memory accesses from the application and the GC so that they follow similar memory access paths, thereby (1)reducing the local-memory working set and (2) improving remote-memory prefetching through simplified memory access patterns. We implemented MemLiner in two widely-used GCs in OpenJDK: G1 and Shenandoah. Our evaluation with a range of widely-deployed cloud systems shows MemLiner improves applications’ end-to-end performance by up to 2.5×.

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@inproceedings {280862,
author = {Chenxi Wang and Haoran Ma and Shi Liu and Yifan Qiao and Jonathan Eyolfson and Christian Navasca and Shan Lu and Guoqing Harry Xu},
title = {{MemLiner}: Lining up Tracing and Application for a {Far-Memory-Friendly} Runtime},
booktitle = {16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22)},
year = {2022},
isbn = {978-1-939133-28-1},
address = {Carlsbad, CA},
pages = {35--53},
url = {},
publisher = {USENIX Association},
month = jul

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