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Record W2006432698 · doi:10.1145/2552999.2553002

Parallelism in Ada

2013· article· en· W2006432698 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

VenueACM SIGAda Ada Letters · 2013
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsGeneral Dynamics (Canada)
Fundersnot available
KeywordsComputer scienceTask parallelismParallelism (grammar)ConcurrencyParallel computingInstruction-level parallelismImplicit parallelismData parallelismTask (project management)Divide and conquer algorithmsSyntaxsortProgramming language

Abstract

fetched live from OpenAlex

Parallel programming is expected to become more the norm as multi-core and many-core processors gain more widespread use. Ada has always had excellent concurrency support, but could be improved in the area of parallel programming. Specifically, divide and conquer parallelism via parallel loops and subprograms are difficult to write without some sort of library support. In this paper we describe a proposal that combines the use of task pools and parallelism managers to provide parallelism capabilities to real-time Ada applications, including Ravenscar applications. This work complements the syntax enhancements that we previously proposed, so that together they facilitate the writing of parallel applications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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