OpenMOLE (Open MOdeL Experiment) has been developed since 2008 as a free and open-source platform. It offers tools to run, explore, diagnose and optimize your numerical model, taking advantage of distributed computing environments. With OpenMOLE you can explore your already developed model, in any language (Java, Binary exe, NetLogo, R, SciLab, Python, C++...).

• The stable version is available on openmole.org.
• A fresh build of the developement version is available on next.openmole.org.

OpenMOLE is distributed under the AGPLv3 free software license.

Before you use OpenMOLE, you need:

• a program you want to study
• to be able to run this program using a command line
• to be able to set some inputs of the program
• to be able to get some outputs variable or some output files out of this program

Then use OpenMOLE:

• embed the executable of your program in OpenMOLE using (5 minutes)
• use one of the distributed exploration algorithms provided by OpenMOLE (5 minutes)
• launch the exploration indeferently on your laptop (10 seconds)
• or on a distributed execution environment with thousands of machines (1 minute).

To summarize, you can model exploration processes at scale reusing legacy code and advanced numeric methods in approximately 10 minutes.

To checkout OpenMOLE you can play with to the demo site (this site is wiped out every few hours). You should click on the little cart and try out some of the market place examples.

• Expressive syntax – A Domain Specific Language to describe your exploration processes,
• Transparent distributed computing – Zero-deployment (no installation step) approach to distribute the workload transparently on your multi-core machines, desktop-grids, clusters, grids, ...
• Works with your programs – Embed user’s executables (Java, Binary exe, NetLogo, R, Scilab, Python, C++, Docker Container...),
• Scalable – Handles millions of tasks and TB of data,
• Advanced methods – Advanced numerical experiments (design of experiments, optimization, calibration, sensitivity analysis, ...).

• Workflow plateform – Design scientific workflows that may use legacy code,
• Distributed genetic algorithms - Distribute the computation of your fitness functions,
• Distributed computing - A high level aproach to distributed computing.