High‐performance extended actors
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".