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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".