Bibliographic record
Abstract
The Ada programming language is seemingly well-positioned to take advantage of emerging multi-core technologies. While it has always been possible to write parallel algorithms in Ada, there are certain classes of problems however, where the level of effort to write parallel algorithms outweighs the ease and simplicity of a sequential approach. This can result in lost opportunities for parallelism and slower running software programs. Languages such as Cilk++ and OpenMB provide expressive mechanisms to add parallelism to code using a C++ based syntax by adding special syntactic directives where parallelism is desired. This paper explores Ada's concurrency features to see whether it is possible to easily inject similar iterative and recursive parallelism to code written in Ada, without having to resort to special language extensions or non-standard language features. This paper identifies a "work-seeking" technique, which can be viewed as a form of compromise between work-sharing and work-stealing, two other existing strategies. The paper presents performance results to illustrate the benefits of use for the generics and goes on to suggest how parallelism pragmas could possibly be added to the Ada language to further facilitate writing parallel applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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.
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".