Group Mutual Exclusion by Fetch-and-increment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 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 it