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Record W4386803560 · doi:10.1002/spe.3262

High‐performance extended actors

2023· article· en· W4386803560 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSoftware Practice and Experience · 2023
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceScalabilityConcurrencyDebuggingRobustness (evolution)Distributed computingShared memoryTRACE (psycholinguistics)Decoupling (probability)Ask priceParallel computingOperating system

Abstract

fetched live from OpenAlex

Abstract Actors are a popular mechanism for indirectly expressing concurrency. This article examines an implementation in the concurrent dialect of C ++, C ++, which runs actors on shared‐memory multi‐processor computers. The C ++ actor system targets 32–256+ multi‐core shared‐memory computers that form the backbone of high‐performance computing, rather than distributed actor communication or robust execution via parentage fallback used by other actor systems. Five new mechanisms are presented to achieve expressibility, robustness, high performance, and scalability of actor applications across multiple cores: explicit life time (storage management) of actors and messages, combining actors and coroutines, a forward message‐trace and backward message‐return for debugging and failures, a new promise call‐back for ask sends, and an actor implementation that inverts the actor execution‐model by decoupling actor mailboxes with high levels of sharding. Microbenchmarks compare the new actor features with CAF, Protoactor, and classic and typed Akka.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.293
Teacher spread0.275 · 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