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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".