Efficient object-oriented execution strategies for parallel computers
Bibliographic record
Abstract
In the traditional object-oriented languages, the object stands for basic entity with attributes (representing data structures) and methods (services that can be provided by the object). Nevertheless an important part of the object that concerns its behavior is still to be defined precisely. The behavior can he defined as rules for the use of methods such as priority, sequentiality and concurrency. What we propose is to define control objects, managing the object behavior. An important aspect of this approach is the separation of concerns of mechanisms and policies, one of the fundamental principles of open systems. We begin with discussing the idea of control objects during the system development life-cycle using object-oriented software engineering (OOSE) described by Jacobson (see Addison-Wesley, 1992). We then extend the model and discuss the importance of control objects in capturing the behavior of a group of associated objects to perform a task. Encapsulating synchronization and concurrency constraints in control objects which provides the basis for parallel execution is also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".