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il y a 4 mois | |
|---|---|---|
| benchmarks | il y a 4 mois | |
| pythonpdevs2.4.2 | il y a 7 mois | |
| web_service | il y a 6 ans | |
| .gitignore | il y a 6 mois | |
| Agents benchmarks.md | il y a 4 mois | |
| Dockerfile | il y a 3 ans | |
| PyPDEVS benchmarks.md | il y a 4 mois | |
| README.md | il y a 9 mois | |
| clean.sh | il y a 3 ans | |
| plotting Agents.md | il y a 4 mois | |
| plotting PyPDEVS.md | il y a 4 mois | |
| pyproject.toml | il y a 4 mois | |
| results_analysis.py | il y a 3 ans | |
| setup.sh | il y a 9 mois | |
| uv.lock | il y a 4 mois |
Here you can find several DEVStone implementations for comparing the main DEVS engines.
Adapted to benchmark Dynamic Structure DEVS
First select a simulator:
Shared parameters:
interval: The interval at which measurements are taken.
model structure: each simulator has its own set of models
For each simulation the parameters and the creation, setup and simulation time are saved to a csv file.
Run the following command, the width and depth step size parameters should be left out, to measure the memory usage of a single simulation, for the same reason the repetitions should be set to one.
uv run testPyPDEVS.py -md 100 -mw 100 -r 1 -O dynamic -ms dLI & PID=$!; python -m procpath record -i 0.1 -f stat -d output.sqlite --stop-without-result "$..children[?(@.stat.pid == $PID)]"
procpath plot -d output.sqlite -f out.svg -q rss