As high-performance computing resources have become increasingly available, new modes of computational processing and experimentation have become possible. This tutorial presents the Extreme-scale Model Exploration with Swift/T (EMEWS) framework for combining existing capabilities for model exploration approaches (e.g., model calibration, metaheuristics, data assimilation) and simulations (or any "black box" application code) with the Swift/T parallel scripting language to run scientific workflows on a variety of computing resources, from desktop to academic clusters to Top 500 level supercomputers. We will present a number of use-cases, starting with a simple agent-based model parameter sweep, and ending with a complex adaptive parameter space exploration workflow coordinating ensembles of distributed simulations. The use-cases are published on a public repository for interested parties to download and run on their own.

From desktop to large-scale model exploration with swift/T

Spagnuolo, Carmine
2017-01-01

Abstract

As high-performance computing resources have become increasingly available, new modes of computational processing and experimentation have become possible. This tutorial presents the Extreme-scale Model Exploration with Swift/T (EMEWS) framework for combining existing capabilities for model exploration approaches (e.g., model calibration, metaheuristics, data assimilation) and simulations (or any "black box" application code) with the Swift/T parallel scripting language to run scientific workflows on a variety of computing resources, from desktop to academic clusters to Top 500 level supercomputers. We will present a number of use-cases, starting with a simple agent-based model parameter sweep, and ending with a complex adaptive parameter space exploration workflow coordinating ensembles of distributed simulations. The use-cases are published on a public repository for interested parties to download and run on their own.
2017
9781509044863
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4721008
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