MuMMI Core is the underlying infrastructure and generalizable component of the MuMMI framework

MuMMI Core v0.1

Released: Nov 16, 2021

MuMMI Core is the underlying infrastructure and generalizable component of the MuMMI framework, which facilitates the coordination of massively parallel multiscale simulations.

MuMMI was developed as part of the Pilot2 project of the Joint Design of Advanced Computing Solutions for Cancer funded jointly by the Department of Energy (DOE) and the National Cancer Institute (NCI).

The Pilot 2 project focuses on developing multiscale simulation models for understanding the interactions of the lipid plasma membrane with the RAS and RAF proteins. The broad computational tool development aims of this pilot are:

• Developing scalable multi-scale molecular dynamics code that will automatically switch between continuum, coarse-grained and all-atom simulations.
• Developing scalable machine learning and predictive models of molecular simulations to:
  • identify and quantify states from simulations
  • identify events from simulations that can automatically signal change of resolution between continuum, coarse-grained and all-atom simulations
  • aggregate information from the multi-resolution simulations to efficiently feedback to/from machine learning tools
• Integrate sparse information from experiments with simulation data.

MuMMI Core exposes abstract functionalities that allow writing the simulation components easily. It is designed to be used in conjunction with a "MuMMI App", which defines the simulation components as well as specifies specific configurations for MuMMI Core. MuMMI Core contains different I/O interfaces, workflow abstractions (job trackers and feedback managers), as well as several additional utilities.

Publications

MuMMI framework has been described in the following publications.

1. Bhatia et al. Generalizable Coordination of Large Multiscale Ensembles: Challenges and Learnings at Scale. In Proceedings of the ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC '21, Article No. 10, November 2021. doi:10.1145/3458817.3476210.

2. Di Natale et al. A Massively Parallel Infrastructure for Adaptive Multiscale Simulations: Modeling RAS Initiation Pathway for Cancer. In Proceedings of the ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC '19, Article No. 57, November 2019. doi:10.1145/3295500.3356197.
   Best Paper at SC 2019.

3. Ingólfsson et al. Machine Learning-driven Multiscale Modeling Reveals Lipid-Dependent Dynamics of RAS Signaling Protein. Proceedings of the National Academy of Sciences (PNAS), accepted, 2021. preprint.

Requirements

• Python 3.7
• A compatible MuMMI application (e.g. MuMMI RAS)

Installation

git clone https://github.com/mummi-framework/mummi-core
cd mummi-core
pip3 install .

export MUMMI_ROOT=/path/to/outputs
export MUMMI_CORE=/path/to/core/repo
export MUMMI_APP=/path/to/app/repo
export MUMMI_RESOURCES=/path/to/resources

Usage

MuMMI core exposes several command line utilities.

• mummi_monitor: Monitor all running simulations.
• mummi_cmdserver: Setup a command server (for the primary user) to allow other users to query the job status.
• mummi_cmdclient: Command client for the queries enabled by the server.
• bind_global_redis: Bind to the redis cluster (uses environment to fetch host and port).
• bind_local_redis: Bind to one redis node within the redis cluster (uses environment to fetch host and port).

Most of the usage comes through a python interface, by importing mummi_core to leverage the utilities provided here.

Authors and Acknowledgements

MuMMI Core was developed at Lawrence Livermore National Laboratory and the main contributors are:

Harsh Bhatia, Joseph Y Moon, Francesco Di Natale, Joseph R Chavez, James Glosli, and Helgi I Ingólfsson.

MuMMI was funded by the Pilot 2 project led by Dr. Fred Streitz (DOE) and Dr. Dwight Nissley (NIH). We acknowledge contributions from the entire Pilot 2 team.

This work was performed under the auspices of the U.S. Department of Energy (DOE) by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Contact: Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550.

Contributing

Contributions may be made through pull requests and/or issues on github.

License

MuMMI Core is distributed under the terms of the MIT License.

Livermore Release Number: LLNL-CODE-827197