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Record W2996001114 · doi:10.29007/brkj

A Software Architecture for Handling Complex Critical Section Constraints on Multiprocessors in a Fault-Tolerant Real-Time Embedded System

2019· article· en· W2996001114 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEPiC series in computing · 2019
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsYork University
Fundersnot available
KeywordsComputer scienceCritical sectionFault toleranceMultiprocessingTask (project management)ArchitectureSection (typography)SoftwareEmbedded systemState (computer science)Distributed computingParallel computingOperating systemProgramming languageEngineering

Abstract

fetched live from OpenAlex

In a real-time embedded system which uses a primary and an alternate for each real-time task to achieve fault tolerance, there is a need to allow both primaries and alternates to have critical sections/segments in which shared data structures can be read and updated while guaranteeing that the execution of any part of one critical section will not be interleaved with or overlap with the execution of any part of a critical section belonging to some other primary or alternate which reads and writes on those shared data structures. In this paper a software architecture is presented which effectively handles critical section constraints where both primaries and alternates may have critical sections which can either overrun or underrun, while still guaranteeing that all primaries or alternates that do not overrun will always meet their deadlines while keeping the shared data in a consistent state on a multiprocessor in a fault tolerant real-time embedded system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.281
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it