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.
Install libEnsemble and its dependencies from PyPI using pip:
pip install libensemble
Other install methods are described in the docs.
Support:
- Ask questions or report issues on GitHub.
- Email
libEnsemble@lists.mcs.anl.govto 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}
}