Use Case

Water Resource Management Using OPENMORDM Data

P. Reed (Cornell) with G. Characklis (UNC Chapel Hill), K. Keller (Penn State). Testing and evaluation of the MORDM framework will leverage a collective partnership between Reed and the water utilities from the North Carolina cities of Cary, Durham, Raleigh, and Chapel Hill

What?

  • On-demand simulation of complex water resource management outcomes to achieve substantial beneficial environmental impact on municipalities across the country

Why Aristotle?

  • Coarsely parallel computations started on demand
  • Very large computations, ideal for transfer to AWS or other cloud, to make available to many municipalities

Accomplishments

  • Built and tested Parallel Platypus software (the Python version of Open MORDM) with a VM, container, and container source (Docker file) developed by the Aristotle team for use as a virtual MPI cluster on Aristotle.
  • Added Docker documentation to the Aristotle wiki.
  • Containerized and published build scripts for the Lake Problem code and successfully ran it in MPI across multiple virtual machines using Docker; this will allow distributed scientific software to be executed faster at cloud scale, both in existing institutional cloud, XSEDE resources, and public provider clouds.

Plans

  • Continue development of the Aristotle MPI cluster, i.e., MPI in a container.
  • Benchmark the scalability of the water resource management software stack and investigate whether many containers can be spun up across multiple clouds, including bursting to AWS.
  • Achieve efficient multi-node support, and benchmark the Parallel Platypus VM for scaling.
  • Build and test two additional software batches.



Cloud-based models could help cities and stakeholders make complex water resource management decisions faster
Cloud-based models could help cities and stakeholders make complex water resource management decisions faster

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