Dekker's mutual exclusion algorithm made RW‐safe
Bibliographic record
Abstract
Summary Dekker's algorithm was thought to be safe in an environment without atomic reads or writes where bits flicker or scramble during simultaneous operations. A counter‐example is presented showing Dekker's algorithm is unsafe without atomic read. A modification to the original algorithm is presented making it RW‐safe, allowing threaded systems to be built on low cost/power hardware without atomic read/write. Correctness is verified by means of invariants and UNITY logic. A performance comparison is made for several two‐thread software mutual‐exclusion algorithms to see if the RW‐safe Dekker is competitive. A subset of the two‐thread solutions are then compared in two N‐thread tournament algorithms. The performance results show that the additional checks in the RW‐safe Dekker do not disadvantage the algorithm in comparison with other two‐thread algorithms. The RW‐safe N‐thread tournament algorithms are competitive with the hardware‐assisted Mellor‐Crummey and Scott algorithm. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".