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# Hybrid Parallel Model Checking of Hybrid LTL on Hybrid State Space Representation
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This repository hosts the results for the paper.

The experimental results were obtained on [Magi
cluster](http://magi.univ-paris13.fr/wiki/) of [University Sorbonne Paris
Nord](https://univ-paris13.fr/). We used the partition `COMPUTE` which has 40
processors (two Intel Xeon E5-2650 v3 at 2.30GHz) connected by an InfiniBand
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network, and 64GB of RAM. A total of 5 models from the [Model Checking
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Contest](https://mcc.lip6.fr/models.php) were used in our experiments:
*[Philosophers](models/philo/description.pdf)* (`philo`),
*[RobotManipulation](models/robot/description.pdf)* (`robot`),
*[SwimmingPool](models/spool/description.pdf)*
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(`spool`), *[CircularTrains]((models/train/description.pdf))* (`train`), and
*[TokenRing](models/tring/description.pdf)* (`tring`).
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## Clone this repository:
```
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git clone https://depot.lipn.univ-paris13.fr/PMC-SOG/experiments/hybrid.git && cd hybrid
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```

## Folder Structure

```
.
├── formulas                        # folder containing the LTL\X formulas
├── models                          # folder containing the models
├── results
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│   ├── figures
│   │   ├── compare_distributed     # figures comparing the parallel and distributed version of pmc-sog
│   │   ├── compare_tools           # figures comparing our tool and pnml2lts-mc
│   │   ├── explored-states         # figures showing the states explored by pnml2lts-mc for each formula
│   │   └── time-plots              # figures showing the performance of the tool for each formula
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│   ├── pmc-sog.zip                 # results (logs) related to our model-checker
│   └── pnml2lts-mc.zip             # results (logs) related to the ltsmin model-checker
├── run-experiment.py               # script that run a experiment on the local machine
├── scripts
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│   ├── csv_generator.py            # script that generates a csv file with the results from the logs
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│   ├── formula_generator.py        # script that generates N random LTL\X formulas
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│   ├── oar_formula_generator.py    # script that generates the files to run the experiments on the GRID5000 cluster
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│   └── plot-results.ipynb          # Python notebook that allows to generate the figures and interact with the results
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│   └── plot-results.py             # script that generates the figures (if you do not want to open the notebook)
│   └── sbatch_formula_generator.py # script that generates the sbatch files to generate formulas on Magi cluster
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│   └── sbatch_generator.py         # script that generates the sbatch files to run the experiments on Magi cluster
└── tools
    ├── pmc-sog                     # binary of our model-checker
    └── pnml2lts-mc                 # binary of the ltsmin model-checker
```

## Run experiments

In the following we provide the steps to run the experiments. We tested this
on Ubuntu 18.04.4 LTS.

### Install dependencies

  - `sudo apt install git python3`
  - `pmc-sog`: Please read the [installation notes](https://depot.lipn.univ-paris13.fr/PMC-SOG/mc-sog).
  - `pnml2lts-mc`: Download from [here](http://github.com/utwente-fmt/ltsmin/releases/download/v3.0.2/ltsmin-v3.0.2-linux.tgz).


The binaries `pmc-sog` and `pnml2lts-mc` must be copied in the `tools` folder.

### Local Run

To run the experiments on a local machine, the script `run-experiment.py` can
be used.

`./run-experiment.py --processes 1 --threads 8 --tool pmc-sog --model-name=philo --model-instance=philo10 --formula=1 --tool-parameter parallelisation=otfP --tool-parameter strategy="Cou99 (poprem)"`

```
./run-experiment.py -h
usage: run-experiment.py [-h] --processes PROCESSES --threads THREADS --tool
                         {pmc-sog,pnml2lts-mc} --model-name MODEL_NAME
                         --model-instance MODEL_INSTANCE --formula FORMULA
                         [--tool-parameter PARAMETERS]

Run an experiment on the local machine

optional arguments:
  -h, --help            show this help message and exit
  --processes PROCESSES
                        number of process
  --threads THREADS     number of threads
  --tool {pmc-sog,pnml2lts-mc}
                        model-checker tool
  --model-name MODEL_NAME
                        Name of the model (e.g. philo)
  --model-instance MODEL_INSTANCE
                        Instance of the model (e.g. philo10)
  --formula FORMULA     Number of the property to verify
  --tool-parameter PARAMETERS
                        Parameter of the tool
```

### Cluster (slurm) Run

- Run the script that generates slurm batch:
  - `./scripts/sbatch_generator.py`
- Execute the experiment with `sbtach`:
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  - `sbatch slurm/experiments/pmc-sog/pmc-sog_otfP_default/philo/n1-th8.sbatch`
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## Generate Figures

In the following we provide the steps to generate the figures. We tested this
on Ubuntu 18.04.4 LTS.

### Install dependencies
  - `sudo apt install git unzip wget libfuse2 libnss3 libgdk-pixbuf2.0-0 libgtk-3-0 libx11-xcb1 libxss1 libasound2 python3 python3-pip`
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  - `pip3 install --user pandas plotly psutil requests kaleido`
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### Decompress results
  - `cd results`
  - `unzip -q pmc-sog.zip`
  - `unzip -q pnml2lts-mc.zip`
  - `cd ..`

### Generate Figures
  - `cd scripts`
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  - `python3 csv_generator.py`
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  - `python3 plot-results.py`

## Authors

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- Kais Klai (University of Paris 13, Sorbonne Paris Cite CNRS UMR 7030 LIPN
- Chiheb Ameur Abid (Faculty of Sciences of Tunis, University of Tunis El Manar, 2092, Tunis, Tunisia)
- Jaime Arias (University of Paris 13, Sorbonne Paris Cite CNRS UMR 7030 LIPN)

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## Abstract
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In this paper, we propose a hybrid parallel model checking algorithm for both
shared and distributed memory architectures. The model checking is performed
simultaneously with a parallel construction of system state space by
distributed multi-core machines. The representation of the system's state
space is a hybrid graph called Symbolic Observation Graph (SOG) combining
symbolic representation of its nodes (sets of single states) and explicit
representation of arcs. The SOG is adapted in order to allow the preservation
of both state and event-based LTL formulae (hybrid LTL) i.e., the atomic
propositions involved in the formula to be checked could be either
state-based or event-based propositions.

We have implemented the proposed model checker within a C++ prototype and
compared our preliminary results with the state of the art model checkers.