Bibliographic record
Abstract
The group mutual exclusion (GME) problem (also called the room synchronization problem) arises in various practical applications that require concurrent data sharing. Group mutual exclusion aims to achieve exclusive access to a shared resource (a shared room) while facilitating concurrency among non-conflicting requests. The problem is that threads with distinct interests are not allowed to access the shared resource concurrently, but multiple threads with same interest can. In Blelloch et al. (2003), the authors presented a simple solution to the room synchronization problem using fetch8add ( F 8 A ) and test-and-set ( T 8 S ) atomic operations. This algorithm has O ( m ) remote memory references (RMRs) in the cache coherent (CC) model, where m is the number of forums. In Bhatt and Huang (2010), an open problem was posed: “ Is it possible to design a GME algorithm with constant RMR for the CC model using fetch8add instructions? ” This question is partially answered in this article by presenting a group mutual exclusion algorithm using fetch-and-increment instructions. The algorithm is simple and scalable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".