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Record W2883140496 · doi:10.1145/3241950.3241953

Synchronous Signals

2018· article· en· W2883140496 on OpenAlexaff
Brad Moore

Bibliographic record

VenueACM SIGAda Ada Letters · 2018
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsGeneral Dynamics (Canada)
Fundersnot available
KeywordsComputer scienceSynchronizingConcurrencySynchronization (alternating current)AbstractionCode (set theory)Interface (matter)Blocking (statistics)Concurrency controlParallel computingDistributed computingProgramming languageComputer networkTransmission (telecommunications)Set (abstract data type)Telecommunications

Abstract

fetched live from OpenAlex

In Ada 2012, the language expanded its support for concurrency with the addition of the Synchronous Barriers library package to the Real-Time Systems annex[1]. This package provides a mechanism to synchronize a group of tasks after the number of blocked tasks reaches a specified count value. One use for this feature is to interleave sequential processing with concurrent or parallel processing. For this usage, two syncrhonous barrier objects can be utilized where one barrier manages the transition from parallel to sequential code, and the other barrier manages the transition back from sequential to parallel code. In general, performance can be improved by minimizing the amount of synchronization in an application. The more that threads of execution can proceed independentally without interference with other threads, the more likely that the available CPUs can focus on completing the independent tasks rather than spending time synchronizing with the other threads. A Synchronous Signal is a synchronization primitive that provides a similar abstraction as a Synchronous Barrier, except it can reduce the amount of synchronization needed by a factor of two. In addition, only one object is needed, to manage both transitions instead of two. In this paper, the abstraction is explored and an interface to use the abstraction is presented. Two forms of the abstraction are considered; a blocking form and a non-blocking form, and the performance measurements are reported and compared against Synchronous Barriers usage. Finally, these examples are also compared and considered for use in a Ravenscar environment.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0340.012

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 head0.012
GPT teacher head0.237
Teacher spread0.224 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

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