OpenMOLE (Open MOdeL Experiment) makes it simple to execute your programs on distributed computing environments. If you want to execute the same program for many different inputs (parameters or datasets), OpenMOLE is the tool that you need. The typical usage of OpenMOLE are high performance model calibration, model exploration, machine learning, optimization, data processing.

  • Works with your programs – Java, Binary exe, NetLogo, R, SciLab, Python, C++…
  • Distributed computing – Works on your multi-core machines, clusters, grids, desktop grid.
  • Expressive – Graphical and scripted workflow system to describe your naturally parallel processes.
  • Scalable – Handles millions of tasks, years of computation, and GBs of data.
  • Mature – Developed since 2008 and widely used.
  • Open – AGPLv3 free software license.

  • TEAM

    Romain REUILLON, responsable scientifique
    Géographie-citées, ISC-PIF/CNRS

    Mathieu LECLAIRE, Ingénieur de Recherche CNRS
    ISC-PIF/CNRS

    Guillaume CHÉREL, Ingénieur de Recherche CNRS
    ISC-PIF/CNRS

ACCESS THIS SERVICE

To access this service, you must apply online and, at the same time, contact the team on the OpenMOLE chat.

PROJECTS

PUBLICATIONS

Citer OpenMOLE

Romain Reuillon, Mathieu Leclaire, Sebastien Rey-Coyrehourcq, OpenMOLE, a workflow engine specifically tailored for the distributed exploration of simulation models published in Future Generation Computer Systems, 2013BibTex

Publications

Reuillon, R., Leclaire, M., & Rey-Coyrehourcq, S. (2013). OpenMOLE, a workflow engine specifically tailored for the distributed exploration of simulation models. Future Generation Computer Systems, 29(8), 1981–1990. https://doi.org/http://dx.doi.org/10.1016/j.future.2013.05.003
Cottineau, C., Chapron, P., & Reuillon, R. (2015). Growing Models from the Bottom Up. an Evaluation-Based Incremental Modelling Method (EBIMM) Applied to the Simulation of Systems of Cities. Journal of Artificial Societies and Social Simulation, 18(4). https://doi.org/10.18564/jasss.2828
Chérel, G., Cottineau, C., & Reuillon, R. (2015). Beyond Corroboration: Strengthening Model Validation by Looking for Unexpected Patterns. PLOS ONE, 10(9), e0138212. https://doi.org/10.1371/journal.pone.0138212
Alvarez, I., Aldama, R. D., Martin, S., & Reuillon, R. (2013). Assessing the Resilience of Socio-ecosystems: Coupling Viability Theory and Active Learning with Kd-trees. Application to Bilingual Societies. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, 2776–2782.
Passerat-Palmbach, J., Reuillon, R., Mazel, C., & Hill, D. R. C. (2013). Prototyping parallel simulations on manycore architectures using Scala: A case study. High Performance Computing and Simulation (HPCS), 2013 International Conference, 405–412. https://doi.org/10.1109/HPCSim.2013.6641447
Passerat-Palmbach, J., Caux, J., Le Pennec, Y., Reuillon, R., Junier, I., Kepes, F., & Hill, D. R. C. (2013). Parallel Stepwise Stochastic Simulation: Harnessing GPUs to Explore Possible Futures States of a Chromosome Folding Model Thanks to the Possible Futures Algorithm (PFA). Proceedings of the 2013 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, 169–178. https://doi.org/10.1145/2486092.2486114
Cottineau, C., Chapron, P., & Reuillon, R. (2015). An incremental method for building and evaluating agent-based models of systems of cities. Journal of Artificial Societies and Social Simulation (JASSS).
Schmitt, C., Rey, S., Reuillon, R., & Pumain, D. (2015). Half a billion simulations: Evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model. Environment and Planning B., 42(2), 300–315.
Mesmoudi, S., Alvarez, I., Martin, S., Reuillon, R., Sicard, M., & Perrot, N. (2014). Coupling geometric analysis and viability theory for system exploration: Application to a living food system. JOURNAL OF PROCESS CONTROL, 24(12, SI), 18–28. https://doi.org/10.1016/j.jprocont.2014.09.013
Reuillon, R., Schmitt, C., De Aldama, R., & Mouret, J.-B. (2015). A New Method to Evaluate Simulation Models: The Calibration Profile (CP) Algorithm. Journal of Artificial Societies and Social Simulation, 18(1), 12. http://jasss.soc.surrey.ac.uk/18/1/12.html
Reuillon, R., Chuffart, F., Leclaire, M., Faure, T., Dumoulin, N., & Hill, D. R. C. (2010). Declarative task delegation in OpenMOLE. In W. W. Smari & J. P. McIntire (Eds.), HPCS (pp. 55–62). IEEE. http://dblp.uni-trier.de/db/conf/ieeehpcs/ieeehpcs2010.html#ReuillonCLFDH10
Rouquier, J.-B., Alvarez, I., Reuillon, R., & Wuillemin, P.-H. (2015). A kd-tree algorithm to discover the boundary of a black box hypervolume. Annals of Mathematics and Artificial Intelligence, 1–16. https://doi.org/10.1007/s10472-015-9456-8
Reuillon, R., Leclaire, M., & Passerat-Palmbach, J. (2015). Model Exploration Using OpenMOLE - a workflow engine for large scale distributed design of experiments and parameter tuning. CoRR, abs/1506.04182. http://dblp.uni-trier.de/db/journals/corr/corr1506.html#ReuillonLP15