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Record W4248424962 · doi:10.1145/354222.353174

Object-oriented real-time concurrency

2000· article· en· W4248424962 on OpenAlexaff
Peter A. Buhr, Ashif S. Harji, Philipp E. Lim, Jiongxiong Chen

Bibliographic record

VenueACM SIGPLAN Notices · 2000
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceConcurrencyDistributed computingScheduling (production processes)ExtensibilityPredictabilityPriority inheritanceConcurrent object-oriented programmingProgramming languageDynamic priority schedulingOperating systemProgramming paradigmReactive programmingRate-monotonic schedulingInductive programming

Abstract

fetched live from OpenAlex

The primary goal of a real-time system is predictability. Achieving this goal requires all levels of the system to work in concert to provide fixed worst-case execution-times. Un-fortunately, many real-time systems are overly restrictive, providing only ad-hoc scheduling facilities and basic concurrent functionality. Ad-hoc scheduling makes developing, verifying, and maintaining a real-time system extremely difficult and time consuming. Basic concurrent functionality forces programmers to develop complex concurrent programs without the aid of high-level concurrency features.Encouraging the use of sophisticated real-time theory and methodology, in conjunction with high-level concurrency features, requires flexibility and extensibility. Giving real-time programmers access to the underlying system data-structures makes it possible to interact with the system to incorporate new ideas and fine-tune specific applications. This paper explores this approach by examining its effect on a selection of crucial real-time issues: real-time monitors, timeouts, dynamic-priority scheduling and basic priority inheritance. The approach is implemented in μC++ .

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.006

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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2000
Admission routes1
Has abstractyes

Explore more

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