Key questions that scientists and engineers typically want to address can be formulated in terms of predictive science. Questions such as: "How well does my computational model represent reality?", "What are the most important parameters in the problem?", and "What is the best next experiment to perform?" 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.
Research on dynan1ical processes \Vithin the Earth and planets increasingly relies upon sophisticated, large-scale cotnputational models. In1proved understanding of fundamental physical processes such as mantle convection and the geodynamo, n1agma dynamics, cn1stal and lithospheric defonnation, earthquake nucleation, and seismic \vave propagation, are heavily dependent upon better numerical modeling. Surprisingly, the rate-lin1iting factor for progress in these areas is not just computing hardware, as was once the case. Rather, advances in software are not keeping pace \Vith the recent improven1ents in hard\vare. Modeling tools in geophysics are usually developed and maintained by individual scientists, or by stnall groups. But it is difficult for any individual, or even a stnall group, to keep up with sweeping advances in computing hard\vare, parallel processing sofuvare, and numerical modeling methodology.We will focus on the challenges faced by computational geophysics and the response of a con1munity initiative in the United States called the Computational Infrastructure for Geodynan1ics (CIG). Instead ofrevie\ving all of the activities CIG has been involved with, \Ve will focus on just a few so as to describe the multiple ways that a virtual organization developed and used software \Vithin the rapidly evolving backdrop of cotnputational science. We will focus on the scientific topics of n1antle convection, tectonics, and co1nputational seistnology, although CIG has also been deeply involved with magma dynan1ics and the geodynamo.Mantle convection is at the heart of understanding ho\v the Earth works, but the process remains poorly understood at best. Progress on fundamental questions, such as the dynamic origin of tectonic plates, layering and stratification \Vithin the mantle, geochemical reservoirs, the them1al history of the Earth, the interpretation of tomography, and the source of volcanic hotspots, all require an interdisciplinary approach. Ntnnerical models of tnantle convection n1ust therefore assimilate 49
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