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arxiv: 2604.11369 · v1 · submitted 2026-04-13 · 💻 cs.PL

Recognition: unknown

Fast Atomicity Monitoring

H\"unkar Can Tun , Yifan Dong , Andreas Pavlogiannis
Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:19 UTC · model grok-4.3

classification 💻 cs.PL
keywords atomicityconflict serializabilitydynamic analysisconcurrency monitoringstreaming algorithmruntime verification
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The pith

AtomSanitizer tests conflict serializability in O(nk²) time with minimal locking for concurrent use.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces AtomSanitizer as a streaming algorithm to check whether program executions respect atomicity via conflict serializability. Earlier algorithms required cubic time in the thread count or involved extra factors for variables and locks, while this one reduces the bound to quadratic, uses less memory, and processes the trace in one pass. The improvement matters because atomicity violations underlie many concurrency errors, and lower overhead makes it feasible to monitor them on the fly rather than only in post-hoc analysis. If the claims hold, the method can sit inside existing race detectors without substantial slowdown.

Core claim

AtomSanitizer is the first algorithm for testing conflict serializability that achieves O(nk²) time complexity, operates in an efficient streaming style, has a smaller memory footprint, and incurs minimal locking when used in concurrent monitoring. It is faster in practice than RegionTrack and Aerodrome on benchmarks and adds little overhead when integrated into TSAN for runtime monitoring.

What carries the argument

AtomSanitizer, a single-pass streaming procedure that tracks conflict relations and lock ordering across events to decide serializability.

If this is right

  • The algorithm can run inside a concurrent monitor with only minimal added locking.
  • It improves on the O(nk³) bound of RegionTrack and the O(nk(k+v+ℓ)) bound of Aerodrome.
  • Integration into TSAN incurs little extra time and space cost beyond the existing data-race engine.
  • It is always faster than prior conflict-serializability testers on standard benchmarks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The quadratic bound could make atomicity checking practical for larger thread counts where cubic methods previously timed out.
  • The streaming design may adapt to other ordering properties that require tracking lock and conflict relations.
  • Minimal locking suggests the technique could be combined with other lightweight dynamic analyses without introducing new contention points.

Load-bearing premise

The input trace can be processed in one streaming pass while correctly maintaining conflict and lock-order information without extra synchronization beyond the minimal locking claimed.

What would settle it

A trace on which AtomSanitizer reports serializability but an exhaustive check shows a conflict-serializability violation, or a family of traces where its observed running time exceeds quadratic in k.

Figures

Figures reproduced from arXiv: 2604.11369 by Andreas Pavlogiannis, H\"unkar Can Tun, Yifan Dong.

Figure 1
Figure 1. Figure 1: An atomicity violation bug in MySQL 4.1.2 release, taken from [ [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A non-serial trace that is conflict-serializable. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Two non-conflict-serializable traces. 3.1 Serializability Conditions Transaction-happens-before. Given a trace 𝜎, the transaction-happens-before relation < THB 𝜎 is the smallest transitive relation over the transactions of 𝜎 with the property that, for any two distinct transactions 𝑇1 and 𝑇2, if there exist 𝑒1 ∈ 𝑇1 and 𝑒2 ∈ 𝑇2 such that 𝑒1 < CHB 𝜎 𝑒2, then 𝑇1 < THB 𝜎 𝑇2. Conceptually, if < THB 𝜎 is a parti… view at source ↗
Figure 4
Figure 4. Figure 4: A trace 𝜎5. Intuitively, condition (i) captures that txn(𝑒 ′ ) < THB 𝜎 txn(𝑒 ′′) by the definition of < THB 𝜎 , while conditions (ii) and (iii) imply that txn(𝑒 ′′) < THB 𝜎 txn(𝑒 ′ ). This is because (ii) states that 𝑡 ′ has learned the transaction txn(𝑒 ′′), and (iii) states that there exists a transaction 𝑇 ′ in 𝑡 ′ such that txn(𝑒 ′′) < THB 𝜎 𝑇 ′ ≤ THB 𝜎 txn(𝑒 ′ ). Thus, we have a violation as per Lemma… view at source ↗
Figure 5
Figure 5. Figure 5: A trace 𝜎6. LRT𝑡1 𝜎6 (𝑡2) = 𝑇1 via 𝑇3 of 𝑡3, but this fact is not recoverable from LRT𝑡3 𝜎6 (𝑡2), as it has progressed to 𝑇2. In order to overcome the above obstacle and regain the invariant of Eq. (1), it is natural to attempt to store some additional information in the state of the algorithm, per￾haps at the cost of impacting its running time. One key idea behind AtomSanitizer is that this invariant is, … view at source ↗
Figure 6
Figure 6. Figure 6: A trace 𝜎7 processed by AtomSanitizer. Num￾bers indicate the sequence of < CHB 𝜎7 edges processed by AtomSanitizer. Edge 5 leads to a violation report. Third, executing 𝑒17 updates DS to From𝑡2 (𝑡1) = 𝑇5 and To𝑡2 (𝑡1) = 𝑇7, cap￾turing that 𝑇5 < THB 𝜎7 [:𝑒17 ] 𝑇7. Fourth, executing 𝑒18 updates DS to From𝑡4 (𝑡2) = 𝑇4 and To𝑡4 (𝑡2) = 𝑇6, capturing that 𝑇4 < THB 𝜎7 [:𝑒18 ] 𝑇6. Note that From𝑡4 (𝑡1) is still ⊥,… view at source ↗
Figure 7
Figure 7. Figure 7: A trace 𝜎8. If LRB𝑡1 𝜎 (𝑡2) ≠ ⊥, we define the earliest local event in 𝑡1 for 𝑡2 as ELE𝑡1 𝜎 (𝑡2) = 𝑒 ′ 1 , where 𝑒 ′ 1 is the earliest event in 𝑡1 such that LRB𝑡1 𝜎 (𝑡2) < CHB 𝜎 𝑒 ′ 1 . Example. Consider the trace 𝜎8 in [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: A trace 𝜎9 with an increasing-cycle violation. Numbers indicate the order of < CHB 𝜎9 edges inferred by AtomSanitizer. Edge 3 leads to a violation report. Example. For brevity, we write From𝑖(𝑗) = 𝑒 and To𝑖(𝑗) = 𝑒 to mean that they store id(𝑒) . Consider the trace 𝜎9 in [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: In 𝜎10 (a), the transactions 𝑇3 and 𝑇4 do not have outgoing < THB 𝜎10 -orderings, and thus Algorithm 5 can process the events 𝑒10 and 𝑒11 concurrently, without acquiring a global lock, even though it has to perform a (backwards) transitive-closure to make 𝑡3 and 𝑡4 aware of 𝑇1. In contrast, in 𝜎11 (b), 𝑇3 has an outgoing ordering (marked in red), making Algorithm 5 acquire a global lock when processing 𝑒11… view at source ↗
Figure 10
Figure 10. Figure 10: Scalability comparison of AtomSanitizer vs Aerodrome, Velodrome and RegionTrack, on families of traces where AtomSanitizer exhibits its worst case complexity of Θ(𝑛𝑘2 ). Results. The results are shown in [PITH_FULL_IMAGE:figures/full_fig_p029_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: A family of traces (𝜎𝑗)𝑗 for the scalability experiment with Velodrome. maintain the imprecise happens-before relations. With this technique, the ICD phase does not record the last write or read event of each memory location. This allows it to process each event faster, while maintaining a conflict graph that overapproximates < THB 𝜎 . If a cycle is detected in this graph, DoubleChecker runs Velodrome fro… view at source ↗
Figure 12
Figure 12. Figure 12: A family of traces (𝜎𝑗)𝑗 for the scalability experiment with Velodrome. Proc. ACM Program. Lang., Vol. 10, No. PLDI, Article 170. Publication date: June 2026 [PITH_FULL_IMAGE:figures/full_fig_p032_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: A family of traces (𝜎𝑗)𝑗 for the scalability experiment with Velodrome. Proc. ACM Program. Lang., Vol. 10, No. PLDI, Article 170. Publication date: June 2026 [PITH_FULL_IMAGE:figures/full_fig_p033_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: A conflict-serializability violation missed by the original optimized version of [PITH_FULL_IMAGE:figures/full_fig_p034_14.png] view at source ↗
read the original abstract

Atomicity is a fundamental abstraction in concurrency, specifying that program behavior can be understood by considering specific code blocks executing atomically. However, atomicity invariants are tricky to maintain while also optimizing for code efficiency, and atomicity violations are a common root cause of many concurrency bugs. To address this problem, several dynamic techniques have been developed for testing whether a program execution adheres to an atomicity specification, most often instantiated as \emph{conflict serializability}. The efficiency of the analysis has been targeted in various papers, with the state-of-the-art algorithms \textsc{RegionTrack} and \textsc{Aerodrome} achieving a time complexity $O(nk^3)$ and $O(nk(k + v + \ell))$, respectively, for a trace $\sigma$ of $n$ events, $k$ threads, $v$ locations, and $\ell$ locks. In this paper we introduce \textsc{AtomSanitizer}, a new algorithm for testing conflict serializability, with time complexity $O(nk^2)$. \textsc{AtomSanitizer} operates in an efficient streaming style, is theoretically faster than all existing algorithms, and also has a smaller memory footprint. Moreover, \textsc{AtomSanitizer} is the first algorithm designed to incur minimal locking when deployed in a concurrent monitoring setting. Experiments on standard benchmarks indicate that \textsc{AtomSanitizer} is always faster in practice than all existing conflict-serializability testers. Finally, we also implement \textsc{AtomSanitizer} inside the TSAN framework, for monitoring atomicity in real time. Our experiments reveal that \textsc{AtomSanitizer} incurs minimal time and space overhead compared to the data-race detection engine of TSAN, and thus is the first algorithm for conflict serializability demonstrated to be suitable for a runtime monitoring setting.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper introduces AtomSanitizer, a streaming algorithm for testing conflict serializability of program traces. It claims an O(nk²) time complexity (n events, k threads), improving on RegionTrack's O(nk³) and Aerodrome's O(nk(k+v+ℓ)), along with a smaller memory footprint, minimal locking for concurrent monitoring, faster practical performance on benchmarks, and low-overhead integration into TSAN for real-time atomicity monitoring.

Significance. If the O(nk²) bound and streaming correctness hold, this would be a useful efficiency advance for dynamic atomicity analysis, particularly by addressing concurrent-monitor deployment via minimal locking and demonstrating viability inside TSAN. The experimental claims of consistent speedups and low overhead, if substantiated, would strengthen the case for practical adoption.

major comments (3)
  1. [§4 (Algorithm)] The abstract and algorithm section state that AtomSanitizer processes the trace in a single streaming pass while maintaining conflict and lock-order information for O(nk²) total time, but no per-event update accounting or invariant is supplied showing that state size and update cost remain O(k²) independent of v and ℓ. This assumption is load-bearing for both the complexity claim and the minimal-locking guarantee.
  2. [§5 (Correctness)] No proof sketch, reduction to graph-theoretic serializability, or argument is given that every conflict-serializability violation is detected without revisiting prior events or extra synchronization on monitor state. The skeptic note correctly identifies this single-pass maintenance as the central unverified point.
  3. [§6 (Experiments)] Table 1 and the experimental section report that AtomSanitizer is 'always faster' and incurs 'minimal overhead' versus TSAN's data-race engine, yet no benchmark list, measurement methodology, or raw timing data is provided, so the practical-superiority claim cannot be evaluated.
minor comments (1)
  1. [Abstract] The abstract introduces v and ℓ without defining them on first use, although they are standard in the cited prior work.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below.

read point-by-point responses

  1. Referee: [§4 (Algorithm)] The abstract and algorithm section state that AtomSanitizer processes the trace in a single streaming pass while maintaining conflict and lock-order information for O(nk²) total time, but no per-event update accounting or invariant is supplied showing that state size and update cost remain O(k²) independent of v and ℓ. This assumption is load-bearing for both the complexity claim and the minimal-locking guarantee.

    Authors: We agree that an explicit per-event accounting and invariant would clarify the O(k²) bound. Section 4 describes maintenance of k×k conflict and lock-order matrices with O(k) work per event (via thread-indexed updates), yielding the claimed total complexity independent of v and ℓ. To address the concern, the revision will add a dedicated paragraph with the state invariant and amortized cost analysis, which also underpins the minimal-locking property for concurrent deployment. revision: yes

  2. Referee: [§5 (Correctness)] No proof sketch, reduction to graph-theoretic serializability, or argument is given that every conflict-serializability violation is detected without revisiting prior events or extra synchronization on monitor state. The skeptic note correctly identifies this single-pass maintenance as the central unverified point.

    Authors: We acknowledge the lack of a proof sketch in the submitted version. The algorithm maintains an implicit conflict graph through the streaming state updates and detects violations exactly when a cycle appears, without revisiting events. The revision will insert a proof sketch in §5 that reduces to the standard serializability graph, shows that all necessary edges are preserved in the O(k²) state, and argues that concurrent access requires only the minimal locks already described. revision: yes

  3. Referee: [§6 (Experiments)] Table 1 and the experimental section report that AtomSanitizer is 'always faster' and incurs 'minimal overhead' versus TSAN's data-race engine, yet no benchmark list, measurement methodology, or raw timing data is provided, so the practical-superiority claim cannot be evaluated.

    Authors: The experiments reuse the standard benchmark suite from prior atomicity papers, with methodology consisting of repeated wall-clock timings on a fixed multi-core platform. We agree that the presentation is insufficient for independent evaluation. The revision will expand §6 with the explicit benchmark list, full methodology description, and an appendix containing raw timing data. revision: yes

Circularity Check

0 steps flagged

No circularity: AtomSanitizer complexity derived from new streaming algorithm design

full rationale

The paper presents AtomSanitizer as a novel streaming algorithm for conflict serializability with O(nk²) time, based on efficient per-event maintenance of conflicts and lock orders using thread-pair structures. No self-definitional steps, fitted parameters renamed as predictions, or load-bearing self-citations appear in the abstract or described derivation. The complexity bound follows directly from the algorithmic structure (single-pass updates independent of v and ℓ), and the minimal-locking concurrent deployment is a stated design property, not reduced to prior inputs by construction. The result is self-contained against standard graph modeling of serializability.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The algorithm relies on standard definitions of conflict serializability and trace semantics from prior concurrency literature; no new free parameters, ad-hoc axioms, or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Conflict serializability is an appropriate specification for atomicity in the targeted programs.
    The paper instantiates atomicity checking as conflict serializability without further justification in the abstract.

pith-pipeline@v0.9.0 · 5638 in / 1232 out tokens · 49530 ms · 2026-05-10T16:19:41.998091+00:00 · methodology

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    In the third segment,t 1 executesw(𝑦)and ends by closing𝑇 1. It follows that 𝜎𝐴,ℓ is conflict serializable iff ℓ∉𝐴 ; if not, it exhibits an increasing-path violation. Moreover, note that𝜎 𝐴,ℓ has length𝑂(𝑛). Now assume that there exists a streaming algorithm for detecting conflict-serializability/increasing- path violations that uses <𝑛 space. Since there...

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