Bibliographic record
Abstract
Golab and Ramaraju recently formalized the Recoverable Mutual Exclusion (RME) problem -- a fault-tolerant generalization of Dijkstra's mutual exclusion problem. Several solutions to the RME problem have been proposed since its introduction, and the hardware required to evaluate their performance became available recently following Intel's public launch of Optane Data Center Persistent Memory. In this paper, we present the first experimental evaluation of RME algorithms using an Optane-equipped multiprocessor, with a focus on efficient queue locks. Specifically, we compare Golab and Hendler's recoverable queue lock against Jayanti, Jayanti, and Joshi's, and show that the former is up to 2x faster. Furthermore, we measure the performance penalty of Optane-based RME locks versus DRAM-based conventional locks by comparing the two recoverable locks against an implementation of Mellor-Crummey and Scott's queue lock, and observe that the latter is several-fold faster.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".