The calibration of complex-system models with OpenMOLE
Mathieu Leclaire // Romain Reuillon
ISCPIF // GeodiverCity ERC
Large scale experimentations on models
Methods
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| Optimization | Sensitivity analysis | Design of experiments | Calibration | Data processing |
These methods are time consuming
Naturally parallel algorithms
permit to leverage parallelism
Computing power
- Personal computer // 1 to 8 cores
- Computing server // up to 50 cores
- Cluster // up to 200 cores
- Grid // > 2000 cores
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European Grid Infrastructure |
What does OpenMOLE do ?
| It implements exploration algorithms |  |
| It transparently delegates computational loads to massively parallel environments |  |
Upscaling
Prototype small
Experiment large
A naturally parallel formalism to design experiments
Embed your model as a black box
C
R
C++
Java
Scala
Scilab
Octave
Python
Netlogo
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A Netlogo Task in OpenMOLE GUI
Assign execution environments to tasks
Download: http://www.openmole.org
Chromosome structuring |
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C++ 2 days per simulation 1600 simulations 8.5 years / CPU |
Junier et al., CTCF-mediated transcriptional regulation through cell type-specific chromosome organization in the β-globin locus, Nucleic Acids Research, 2012.
SimTRAP project |
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NetLogo 5 minutes per simulation 100000 simulations 1 year / CPU |
PhD thesis of J. Figuel, Modélisation et simulation des comportements piétonniers dans les espaces de transport – Application aux échanges quai / train de voyageurs.
Simpop project |
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Scala 5 minutes per simulation 360 000 000 simulations 22 years / CPU |
Reuillon et al., Algorithmes évolutionnaires sur grille de calcul pour le calibrage de modéles géographiques, proceedings of France Grilles 2012.
The Bioemergences project |
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C Image processing portal access daily productions 10000 jobs / day |
Ralf Mikut et al, Automated Processing of Zebrafish Imaging Data: A Survey, Zebrafish, 2013