The IPS Framework: A Flexible Approach to Loosely-Coupled Multiphysics Simulations

David E. Bernholdt, Wael R. Elwasif, and Samantha S. Foley* Oak Ridge National Laboratory * Current affiliation: U. Wisconsin-La Crosse

Background and Requirements •  Center for Simulation of RF Wave Interaction with Magnetohydrodynamics (SWIM) – DOE SciDAC program 2005-2011 •  Integrated modeling of fusion plasmas –  Loose coupling of well-established single-physics models in timestepped simulations –  Utilize (nearly) unmodified physics binaries as components –  Support multiple implementations of the same physics –  Data exchange via files

•  Physicists need to be able to create and change simulation driver without assistance •  Physics codes vary widely in scalability, run time •  Run on HPC systems with minimal support 2

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Basic IPS Architecture and Features •  Python-based component framework •  Components are python-wrapped binaries CCA •  Simple component interface

Common Component Architecture

–  Primarily init(), step(), finalize()

•  Framework provides services to connected components

–  Configuration, task, data, resource, event, monitoring

•  Framework runs in a batch allocation, manages resources for components •  Components launched using standard system mechanisms, i.e. mpiexec, aprun •  “Plasma State” holds primary data for exchange –  “Reader-makes-right” model

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Multi-Level Parallelism and Task-and Event-Based Execution 3

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1.  Individual “tasks” (physics executables) can be parallel 2.  Components can launch multiple tasks 3.  Multiple components can run concurrently 4.  Multiple independent simulations can run concurrently Task pools and event service support additional flexibility DAKOTA integration 4

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Is the IPS Right for You? •  Ability to rapidly and easily express and modify overall simulation workflow is more important than absolute performance •  Want to use existing codes as components with little or no modification •  Loosely coupled components –  File-based data exchange

•  Components run long enough that launch costs do not dominate –  IPS uses system parallel launch (ie mpiexec or aprun) for each task

•  Components have very different parallel scalability and execution times –  IPS manages batch allocation internally, and can use multiple-component multiple-data style concurrency to “pack” tasks into available resources 5

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Other IPS Users •  Various fusion integrated modeling in US and maybe Europe •  Computer-Aided Engineering of Batteries •  Multivariate design studies/optimization for industrial applications Possible extensions/future work •  Tighter integration of components and framework (more intrusive) •  In-memory data exchange •  More efficient task launch •  Fault tolerance

–  Preliminary work on smart re-execution of failed tasks

•  Simpler language to express task-based, dependency-driven workflows

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The IPS Framework: A Flexible Approach to Loosely ...

Nov 19, 2013 - Support multiple implementations of the same physics ... Task pools and event service support ... Computer-Aided Engineering of Batteries.

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