High‐contention mutual exclusion by elevator algorithms
Bibliographic record
Abstract
Summary This paper presents new starvation‐free hardware‐assisted and software‐only algorithms for the N‐thread mutual‐exclusion problem. The hardware‐assisted versions use a single atomic‐CAS instruction and no fences. The software‐only algorithms simulate the CAS instruction using a variation of Burns‐Lamport (1 fence) or Lamport's fast algorithm (3 fences). The algorithms are based on Attiya et al, where every thread in the critical section chooses its successor (if one is available). While Attiya et al use a binary tree for this purpose, it can also be done with a linear search. Surprisingly, all software‐only algorithms perform equally well under maximal contention on three different computer architectures; the hardware‐assisted versions perform better under minimal contention. The new algorithms are between −5% to 50% slower for maximal contention than the starvation‐free first‐come first‐served hardware‐assisted MCS algorithm, which uses two atomic instructions (fetch‐store and CAS); they are between 10% to 50% slower than MCS for minimal contention.
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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 | 0.003 |
| 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 | 0.002 |
| 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".