Bibliographic record
Abstract
The mutual exclusion (ME) problem has been of interest to the scientific community since it was first defined by Dijkstra. Various algorithms have been developed to solve the problem, like the MCS and CLH queue-based locks. The problem was generalized into the recoverable mutual exclusion (RME) problem by Golab and Ramaraju to accommodate the possibility of process crash failures. Since then, multiple RME algorithms have been presented in the literature that vary in design and performance. Furthermore, non-uniform memory access (NUMA) architecture has become mainstream in designing modern distributed systems, stimulating the development of NUMA-aware mutex locks. None of the existing NUMA-aware mutex locks are recoverable to the best of our knowledge. In addition, none of the transformation techniques in the literature, such as flat-combining and cohort-locking, is a black-box transformation. Precisely, each of the existing transformation techniques requires specific characteristics of, and possible modifications to, the underlying NUMA-oblivious lock. In this work, we propose the Recoverable Filter (RF) lock, a black-box transformation approach that exploits memory locality to transform a NUMA-oblivious recoverable mutex lock into a NUMA-aware one. Practical experiments are conducted using two existing RME algorithms, Golab and Hendler's (GH) and Jayanti, Jayanti, and Joshi's (JJJ). The two RME locks are transformed into NUMA-aware locks using the proposed RF and the existing cohort algorithms. Results show that, in multi-socket configurations, our transformation boosts the performance of the NUMA-oblivious RME locks by up to 45%. The RME locks transformed using the proposed RF lock are slower than their non-recoverable cohort variants by up to 9%. Outcomes demonstrate that the overhead of our algorithm is minimal when using a single socket. Moreover, a deeper empirical assessment shows that the gap in performance between GH and JJJ is due to the entry section of JJJ, not its exit section.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".