README.rst

libEnsemble: A complete toolkit for dynamic ensembles of calculations

Adaptive, portable, and scalable software for connecting "deciders" to experiments or simulations.

• Dynamic ensembles: Generate parallel tasks on-the-fly based on previous computations.
• Extreme portability and scaling: Run on or across laptops, clusters, and leadership-class machines.
• Heterogeneous computing: Dynamically and portably assign CPUs, GPUs, or multiple nodes.
• Application monitoring: Ensemble members can run, monitor, and cancel apps.
• Data-flow between tasks: Running ensemble members can send and receive data.
• Low start-up cost: No additional background services or processes required.

libEnsemble is effective at solving design, decision, and inference problems on parallel resources.

Quickstart

Installation

Install libEnsemble and its dependencies from PyPI using pip:

pip install libensemble

Other install methods are described in the docs.

Resources

Support:

• Ask questions or report issues on GitHub.
• Email libEnsemble@lists.mcs.anl.gov to request libEnsemble Slack page.
• Join the libEnsemble mailing list for updates about new releases.

Further Information:

• Documentation is provided by ReadtheDocs.
• Contributions to libEnsemble are welcome.
• Browse production functions and workflows in the Community Examples repository.

Cite libEnsemble:

@article{Hudson2022,
  title   = {{libEnsemble}: A Library to Coordinate the Concurrent
             Evaluation of Dynamic Ensembles of Calculations},
  author  = {Stephen Hudson and Jeffrey Larson and John-Luke Navarro and Stefan M. Wild},
  journal = {{IEEE} Transactions on Parallel and Distributed Systems},
  volume  = {33},
  number  = {4},
  pages   = {977--988},
  year    = {2022},
  doi     = {10.1109/tpds.2021.3082815}
}