RMR-Efficient Detectable Objects for Persistent Memory and Their Applications
Bibliographic record
Abstract
We describe a novel construction of arbitrary read-modify-write (RMW) primitives in a persistent shared memory model with process failures. Our construction uses blocking synchronization, in the form of recoverable mutual exclusion (RME), and is optimal in terms of the widely studied remote memory reference (RMR) complexity measure. The implemented objects tolerate either system-wide or independent process crashes, depending on the RME lock used, and also provide detectability for resolving the outcome of operations interrupted by failures. We prove that our construction is RMR-optimal using a reduction back to the RME problem. Our proof technique introduces a novel algorithmic style that enables solving challenging synchronization problems using a common execution path for both the system-wide and independent failure models, which previously required separate analyses, and relies only on a suitable implementation of the detectable base objects in each model to achieve RMR efficiency. Experiments demonstrate that our construction outperforms prior wait-free and lock-free algorithms on a multiprocessor with Intel Optane persistent memory.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".