Bibliographic record
Abstract
Modeling and Simulation (M&S) has become an essential tool in science and engineering due to its ability to represent problems in various disciplines and conduct scientific explorations.To define solutions based on M&S it is important to have formal definitions for the models.In this way, we can define the models with precision, as well as verify and reuse them.The Discrete Event System Specification (DEVS) formalism provides a theoretical formalism for developing M&S based on discrete events.While we can use DEVS to define models precisely, an issue with M&S is that the simulation of complex problems generally requires a significant number of computations, and this leads to large execution times.In addition, deploying simulations in parallel computers presents an additional challenge, the energy high-performance computers need to operate.To address these issues, we propose a methodology to execute DEVS simulation efficiently in modern parallel computer architectures.Our approach provides a simple and error-free method that improves previous approaches by allowing additional parallelism.We propose novel algorithms aimed for multicore computers, and heterogeneous platforms composed by multicore and GPU architectures in the same system.To evaluate our methodology, we provide an extensive performance analysis over a synthetic benchmark and a real-world problem.Our experimental results show how our approach allows us to accelerate simulations up to 32.9 times.We also analyze the energy consumption required by parallel computers to execute simulations.The amount of energy consumed depends on both the execution time and the power used by the hardware.Power is defined as the amount of energy consumed per unit of time.Our experimental findings demonstrate that while parallel execution consumes
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".