A dEVS Simulation Algorithm Based on Shared Memory for Enhancing Performance
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Bibliographic record
Abstract
The Discrete EVent System Specification (DEVS) formalism provides a unified method to define any discrete-event system accurately. As the complexity of the system under study increases, the necessity of simulation engines with higher performance rises. In this research, we present a chained DEVS simulator, a DEVS-compliant, function-oriented simulation algorithm that exploits shared memory patterns to improve the performance of sequential and parallel simulations. We also illustrate the positive impact of this novel approach executing a set of DEVStone synthetic benchmarks and comparing a state-of-the-art simulation engine with an updated version that implements the chained algorithm. Results show that the chained simulator introduces up to 40% less synchronization overhead than the traditional simulation approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 it