Flattened Parallel DEVS Simulations on GPU Architectures
Bibliographic record
Abstract
Discrete Event System Specification (DEVS) is a modeling and simulation of discrete event systems formalism. Most DEVS-based simulators are implemented as sequential programs. However, simulating large-scale complex models in a sequential simulator is impractical (if possible), as simulations may take a long time to execute. A usual technique to speed up simulations is the parallel execution of the simulator. Most parallel discrete-event simulation efforts focus on logical process approaches, resulting in complex simulation architectures. Recent parallelizing efforts lean towards executing the simulators in multicore architectures. Despite promising results, they are limited to the amount of CPU processing cores. In this work, we propose an algorithm to accelerate the execution of DEVS simulations on Graphical Processing Units (GPU) architectures. We show different case studies where the proposed algorithm achieved speedups of up to 12.29 and 16.53 compared to a sequential version.
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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".