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Computer Science > Performance

arXiv:2112.07880 (cs)
[Submitted on 15 Dec 2021]

Title:An Empirical Lower Bound on the Overheads of Production Garbage
Collectors

Authors:Zixian Cai, Stephen M. Blackburn, Michael D. Bond, Martin
Maas
Download PDF

    Abstract: Despite the long history of garbage collection (GC) and
    its prevalence in modern programming languages, there is
    surprisingly little clarity about its true overheads. Even when
    evaluated using modern, well-established methodologies, crucial
    tradeoffs made by GCs can go unnoticed, which leads to
    misinterpretation of evaluation results. In this paper, we 1)
    develop a methodology that allows us to place a lower bound on
    the absolute overhead (LBO) of GCs, and 2) expose key performance
    tradeoffs of five production GCs in OpenJDK 17, a
    high-performance Java runtime. We find that with a modest heap
    size and across a diverse suite of modern benchmarks, production
    GCs incur substantial overheads, spending 7-82% more wall-clock
    time and 6-92% more CPU cycles relative to a zero-cost GC scheme.
    We show that these overheads can be masked by concurrency and
    generous provision of memory/compute. In addition, we find that
    newer low-pause GCs are significantly more expensive than older
    GCs, and sometimes even deliver worse application latency than
    stop-the-world GCs. Our findings reaffirm that GC is by no means
    a solved problem and that a low-cost, low-latency GC remains
    elusive. We recommend adopting the LBO methodology and using a
    wider range of cost metrics for future GC evaluations. This will
    not only help the community more comprehensively understand the
    performance characteristics of different GCs, but also reveal
    opportunities for future GC optimizations.

Comments: Under submission
Subjects: Performance (cs.PF); Programming Languages (cs.PL)
Cite as:  arXiv:2112.07880 [cs.PF]
          (or arXiv:2112.07880v1 [cs.PF] for this version)

Submission history

From: Zixian Cai [view email]
[v1] Wed, 15 Dec 2021 04:51:49 UTC (165 KB)
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