Bibliographic record
Abstract
An implicit (automatic) signal monitor uses a waituntil predicate statement to construct synchronization, as opposed to an explicit-signal monitor using condition variables and signal/wait statements for synchronization. Of the two synchronization approaches, the implicit-signal monitor is often easier to use and prove correct, but has an inherently high execution cost. Hence, its primary use is for prototyping concurrent systems using monitors, where speed and accuracy of software development override execution performance. After a concurrent system is working, any implicit-signal monitor that is a performance bottleneck can be converted to an explicit-signal monitor. Unfortunately, many monitor-based concurrency systems provide only explicit-signal monitors, precluding the design benefits of implicit-signal monitors.This article presents a historical look at the development of the implicit-signal monitor in relation to its counterpart the explicit-signal monitor. An analysis of the different kinds of implicit-signal monitors shows the effects certain design decisions have on the problems that can be solved and the performance of the solutions. Finally, an extensive discussion is presented on simulating an implicit-signal monitor via different explicit-signal monitors. These simulations are reasonably complex, depending on the kind of explicit-signal monitor available for the simulation and the desired semantics required for the implicit-signal monitor. Interestingly, the complexity of the simulations also illustrates certain deficiencies with explicit-signal monitors, which are discussed in detail. Performance comparisons are made among the different simulations with monitors from the concurrent systems PThreads, Java, and μC++.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".