A HIGHLY CONCURRENT GROUP MUTUAL l-EXCLUSION ALGORITHM
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Bibliographic record
Abstract
The asynchronous group mutual exclusion (GME) problem [5] is a generalization of the (ordinary) mutual exclusion problem. Each process requests a forum. When a process is in the critical section (CS), we say its forum is held. The GME problem (i) allows at most one forum to be held at any time and, (ii) when a forum is held, encourages any number of processes requesting that forum to participate in that forum, that is, to be in the CS simultaneously. An extension of this problem, called the group mutual l-exclusion with multiple forum requests (GMLE-M) problem [8], allows each process to request multiple forums and up to l forums to be held simultaneously. In this paper, we give a simple GMLE-M algorithm that facilitates a highly concurrent participation of processes in forums. The algorithm uses a new technique, called automatic joining, that enables processes to enter the CS directly when their forums are being held. This technique can be used in other extensions of the GME problem also, to increase concurrency.
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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 | 0.001 |
| 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