On the Codification of Coordination: An Ontological Tool for Pattern Mining
Bibliographic record
Abstract
One of the challenges developers face when dealing with parallelism is that purely static views of code tend not to reveal internal dynamics and causal relationships that can be problematic. This paper considers parallelism from multiple perspectives—from kinesthetic exercises involving elementary school children, to lines of code spanning six different parallelization mechanisms. We attempt to reconcile these views and develop an ontology designed to support pattern mining in parallel code bases. We show that, both kinesthetically and in the code bases, coordination emerges as a subtle entity that is difficult to identify in a coherent and conceptually concise manner. We believe that low level implementation patterns and micro patterns will be crucial for comprehension of coordination, and propose tool support for effective mining. Further, we suggest a means of accomplishing a semi-automated ontological mapping for parallelization mechanisms, and offer this up to help designers uncover patterns and pattern compositions. 1.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".