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Dynamic load balancing both within and between constituent subsystems is required to achieve ultrascalability in coupled multiphysics and multiscale models. Interconstituent 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(More)
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(More)
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(More)
Research in large-scale distributed systems, such as P2P systems, often relies critically on simulations to validate research results. Though systems such as PlanetLab can be used to test on real networks in some cases, there are still significant practical challenges to evaluating large-scale distributed systems on actual hardware. Actual measured datasets(More)
High-precision accelerator modeling is essential for particle accelerator design and optimization. However, this modeling presents a significant computational challenge. We discuss performance modeling of and computational quality of service (CQoS) results from Synergia2, an advanced particle accelerator simulation code developed under the ComPASS SciDAC-2(More)
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