Detectable Sequential Specifications for Recoverable Shared Objects
Bibliographic record
Abstract
The recent commercial release of persistent main memory by Intel has sparked intense interest in recoverable concurrent objects. Such objects maintain state in persistent memory, and can be recovered directly following a system-wide crash failure, as opposed to being painstakingly rebuilt using recovery state saved in slower secondary storage. Specifying and implementing recoverable objects is technically challenging on current generation hardware precisely because the top layers of the memory hierarchy (CPU registers and cache) remain volatile, which causes application threads to lose critical execution state during a failure. For example, a thread that completes an operation on a shared object and then crashes may have difficulty determining whether this operation took effect, and if so, what response it returned. Friedman, Herlihy, Marathe, and Petrank (DISC'17) recently proposed that this difficulty can be alleviated by making the recoverable objects detectable, meaning that during recovery, they can resolve the status of an operation that was interrupted by a failure. In this paper, we formalize this important concept using a detectable sequential specification (DSS), which augments an object’s interface with auxiliary methods that threads use to first declare their need for detectability, and then perform detection if needed after a failure. Our contribution is closely related to the nesting-safe recoverable linearizability (NRL) framework of Attiya, Ben-Baruch, and Hendler (PODC'18), which follows an orthogonal approach based on ordinary sequential specifications combined with a novel correctness condition. Compared to NRL, our DSS-based approach is more portable across different models of distributed computation, compatible with several existing linearizability-like correctness conditions, less reliant on assumptions regarding the system, and more flexible in the sense that it allows applications to request detectability on demand. On the other hand, application code assumes full responsibility for nesting DSS-based objects. As a proof of concept, we demonstrate the DSS in action by presenting a detectable recoverable lock-free queue algorithm and evaluating its performance on a multiprocessor equipped 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".