Building a Framework for Predictive Science
Michael M. McKerns
Leif Strand
Tim Sullivan
Alta Fang
Michael A.G. Aivazis
Key questions that scientists and engineers typically want to address can be
formulated in terms of predictive science. Questions such as: \textquotedbl{}How well does my
computational model represent reality?\textquotedbl{}, \textquotedbl{}What are the most important
parameters in the problem?\textquotedbl{}, and \textquotedbl{}What is the best next experiment to perform?\textquotedbl{}
are fundamental in solving scientific problems. mystic is a framework for
massively-parallel optimization and rigorous sensitivity analysis that enables
these motivating questions to be addressed quantitatively as global
optimization problems. Often realistic physics, engineering, and materials
models may have hundreds of input parameters, hundreds of constraints, and may
require execution times of seconds or longer. In more extreme cases, realistic
models may be multi-scale, and require the use of high-performance computing
clusters for their evaluation. Predictive calculations, formulated as a global
optimization over a potential surface in design parameter space, may require an
already prohibitively large simulation to be performed hundreds, if not
thousands, of times. The need to prepare, schedule, and monitor thousands of
model evaluations, and dynamically explore and analyze results, is a
challenging problem that requires a software infrastructure capable of
distributing and managing computations on large-scale heterogeneous resources.
In this paper, we present the design behind an optimization framework, and also
a framework for heterogeneous computing, that when utilized together, can make
computationally intractable sensitivity and optimization problems much more
tractable. The optimization framework provides global search algorithms that
have been extended to parallel, where evaluations of the model can be
distributed to appropriate large-scale resources, while the optimizer centrally
manages their interactions and navigates the objective function. New methods
have been developed for imposing and solving constraints that aid in reducing
the size and complexity of the optimization problem. Additionally, new
algorithms have been developed that launch multiple optimizers in parallel,
thus allowing highly efficient local search algorithms to provide fast global
optimization. In this way, parallelism in optimization also can allow us to not
only find global minima, but to simultaneously find all local minima and
transition points -{}- thus providing a much more efficient means of mapping out
a potential energy surface.
predictive science, optimization, uncertainty quantification, verification, validation, sensitivity analysis, parallel computing, distributed computing, heterogeneous computing
DOI10.25080/Majora-ebaa42b7-00d