High‐performance<i>N</i>‐thread software solutions for mutual exclusion
Bibliographic record
Abstract
Summary Software solutions for mutual exclusion developed over a 30‐year period, starting with complex ad hoc algorithms and progressing to simpler formal ones. While it is easy to dismiss software solutions for mutual exclusion, as this family of algorithms is antiquated and most platforms support atomic hardware instructions, there is still a need for these algorithms in threaded, embedded systems running on low‐cost processors lacking atomic instructions. WhileN‐thread solutions are usually short (10–25 lines of code), each is ingenious with exceptionally subtle aspects, often making it difficult to prove correctness or construct an implementation. This work examines correctness and performance of the implementations. An extensive survey of existing algorithms is presented, with explanations of the intuition behind the algorithms and how they work. Several errors were found and corrections made, as well as a few small improvements, in the existing algorithms; two new high‐performance algorithms were developed. Finally, a worst‐case high‐contention performance experiment is performed to compare the algorithms and contrast them with three common locks based on hardware atomic instructions. The results show our two new algorithms are highly competitive with an equivalent hardware lock (Mellor‐Crummey and Scott) over a range of 1–32 processors. Hence, threading is a viable alternative to event‐driven programming for complex embedded systems without atomic instructions. Copyright © 2014 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".