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Korali is a high-performance framework for uncertainty quantification and optimization of large-scale models.
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High-performance framework for uncertainty quantification of large-scale models.
Korali is a high-performance framework for uncertainty quantification of large-scale models. Korali's multi-language interface allows the execution of any type of computational model, either sequential or distributed (MPI), C++ or Python, and even pre-compiled/legacy applications. Korali's execution engine enables scalable sampling on large-scale HPC systems.
Korali provides a simple interface that allows users to easily describe statistical problems and choose the algorithms to solve them, allowing users to apply a wide range of operations on the same problem with minimal re-configuration efforts. Finally, users can easily extend Korali to describe new problems and test new experimental algorithms
Visit:https://www.cse-lab.ethz.ch/korali/ for more information.
See:https://github.com/cselab/korali-apps
The Korali Project is developed and maintained by
- Georgios Arampatzis, garampat at ethz.ch
- Sergio Miguel Martin, martiser at ethz.ch
- Daniel Waelchli, wadaniel at ethz.ch
Director:
- Petros Koumoutsakos, petros at ethz.ch