Parallel simulation of DEVS and Cell-DEVS models on Windows-based PC cluster systems
Bibliographic record
Abstract
parallel simulation, cluster systems The growing popularity of Networks of Workstations (NOW) in scientific computation has drawn increasing interest from the M&S community. This paper addresses the issue of parallel discrete-event simulation of DEVS and Cell-DEVS models on a Microsoft Windows-based cluster system comprising interconnected general-purpose personal computers. We present the architecture and features of PCD++Win, a parallel simulator that takes advantage of the multi-purpose graphical user interface of the DeinoMPI middleware for construction of ad-hoc PC clusters and configuration of simulation environment. This environment significantly reduces the learning curve for general users and the cost of the simulation platform. PCD++Win has been developed using a modular approach that promotes code reuse and allows for easy switching to other middleware technologies. The portability of the simulator is enhanced with multi-platform programming and compilation techniques. Moreover, it leaves open the possibility of further extensions such as Web-based distributed simulation and database-based model construction by leveraging the native support of Microsoft Visual Studio. The experiments demonstrate the capability of the new simulator, making it an ideal M&S toolkit for tapping the computational power of general-purpose desktop computers. 1.
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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".