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Computationally-demanding, parallel coupled models are crucial to understanding many important multi-physics/multiscale phenomena. Load-balancing such simulation son large clusters is often done through off-line, static means that often require significant manual input. Dynamic, runtime load-balancing has been shown in our previous work to be effective, but we still used a manually generated performance...
Dynamic load balancing both within and between constituent subsystems is required to achieve ultra scalability in coupled multi physics and multi scale models. Inter constituent dynamic load balancing requires runtime resizing -- or malleability -- of subsystem processing element (PE) cohorts. In our previous work, we developed and introduced the Malleable Model Coupling Toolkit with a load balance...
Achieving ultra scalability in coupled multiphysics and multiscale models requires dynamic load balancing both within and between their constituent subsystems. Interconstituent dynamic load balance requires runtime resizing -- or malleability -- of subsystem processing element (PE) cohorts. We enhance the Malleable Model Coupling Toolkit's Load Balance Manager (LBM) to incorporate prediction of a...
Model coupling is a method to simulate complex multiphysics and multiscale phenomena. Most approaches involve static data distribution among processes without the consideration of top-level dynamic load balancing. Malleability, the ability to change the number of processes during execution, allows applications to configure themselves to better utilize available system resources. To date, however,...
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