Synchronization for fast and reentrant operating system kernel tracing
Bibliographic record
Abstract
Abstract To effectively trace an operating system, a performance monitoring and debugging infrastructure needs the ability to trace various execution contexts. These contexts range from kernel running as a thread toNon‐Maskable Interrupt(NMI) contexts. Given that any part of the kernel infrastructure used by a kernel tracer could lead to infinite recursion if traced, and because most kernel primitives require synchronization unsuitable for some execution contexts, all interactions of the tracing code with the existing kernel infrastructure must be considered in order to correctly inter‐operate with the existing operating system kernel. This paper presents a new low overhead tracing mechanism and motivates the choice of synchronization sequences suitable for operating system kernel tracing, namelylocal atomic instructionsas main buffer synchronization primitive and theRead–Copy Update(RCU) mechanism to control tracing. It also proposes a wait‐free algorithm extending the time‐base needed by the tracer to 64‐bit on architectures that lack hardware 64‐bit time‐base support. Copyright © 2010 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".