Simple, Fast and Widely Applicable Concurrent Memory Reclamation via Neutralization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reclaiming memory in non-blocking dynamic data structures in unmanaged languages like C/C++ presents a unique challenge due to the risk of use-after-free errors caused by concurrent accesses. Existing safe memory reclamation (SMR) algorithms fall short of satisfying five key properties: high performance, bounded garbage, usability, consistency, and applicability. In particular, bounded garbage and high performance are quite difficult to achieve simultaneously. In this paper, we address this limitation by proposing a new, provably correct technique called neutralization based reclamation (NBR) that neutralizes threads using POSIX signals to provide the synchronization required for safe memory reclamation. NBR uses atomic reads and writes and achieves bounded garbage and high performance without imposing significant overhead on concurrent readers and writers. An extensive experimental evaluation serves to demonstrate the efficiency of our technique across various data structures, reclamation algorithms, and workloads. A detailed survey of popular concurrent data structures suggests NBR is applicable to a wide range of data structures, many of which could not be used with prior SMR algorithms that guarantee bounded garbage.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it