Experimentation on complex-system models with OpenMOLE

Up arrow

Mathieu Leclaire // Romain Reuillon

ISCPIF // GeodiverCity ERC

geod
geod
monkey

Large scale experimentations on models

Methods

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

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

Embed your model as a black box

C
R
C++
Java
Scala
Scilab
Octave
Python
Netlogo
...

Workflow topology

A Task runs an executable,
receives and produces Variables.
Variables navigate from one Task to another by means of Transitions
eventually doing Loops.

The design of experiments

mozaique parameters
Design you experiment in OpenMOLE At runtime
A naturally parallel formalism to design experiments
A Netlogo Task in OpenMOLE GUI
Assign execution environments to tasks

Environments already supported by OpenMOLE


  • multi threading
  • remote SSH server
  • PBS Cluster
  • EGI
  • Dirac
  • soon : SLURM, SGE, other cluster batch systems
  • ... and later : private and academic clouds
Download: http://www.openmole.org

Chromosome structuring

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

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

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

C
Image processing

portal access

daily productions
10000 jobs / day
Ralf Mikut et al, Automated Processing of Zebrafish Imaging Data: A Survey, Zebrafish, 2013