Automatic Generation of Real-Time Aircraft Simulation System Configurations
Bibliographic record
Abstract
Building configurations for real-time aircraft simulation systems is a challenging task. It involves the distribution of the applications among different scheduling processes, bound to different CPU's, in such a way that the applications' priority and expected execution order are taken into account. \n \nIn this thesis, we report on a study conducted at CAE Inc., a world leading manufacturer of flight simulation products, in which we have developed an approach to automatically build configurations. Our approach is based on a greedy algorithm that uses heuristics to distribute many applications into different partitions in such a way that inter-partition communication is minimized, the load across partitions is balanced, and each partition is denoted as a binary tree (the data structure used by the scheduler to run the applications). The configuration is also constrained by the priority and execution time of the applications. \n \nWhen applied to CAE, our approach produces configurations that in most cases outperform or are similar to those generated by a domain expert.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".