Increasing the performance of a Discrete Event System Specification simulator by means of computational resource usage “activity” models
Bibliographic record
Abstract
Domain-specific simulators often have an edge on general-purpose simulators in terms of performance. Their intricate knowledge of the domain allows them to aggressively optimize and take shortcuts. In contrast, simulators for more general formalisms, such as Discrete Event System Specification (DEVS), need to support a wider set of models. Their inability to use domain information prevents DEVS simulators from achieving as high performance as their domain-specific variants. To solve this problem, we introduce a way to enhance the simulation performance of DEVS models through the use of computational resource usage models, often termed “activity” models. These models augment general-purpose DEVS models with domain-specific information, which can be used by the simulator. We apply this information in the context of data structure optimization, load balancing, and model allocation. Activity-awareness is a non-invasive extension to the DEVS formalism, meaning that activity-augmented models remain perfectly valid for use in activity-unaware simulators. Similarly, models without activity can still be simulated by an activity-aware simulator. Our approach is validated by making PythonPDEVS, a Parallel DEVS simulator, activity-aware and evaluating the performance impact on a set of benchmark models.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".