Computational Science Demands a New Paradigm

Abstract

C omputers have become indispensable to scientific research. They are essential for collecting and analyzing experimental data, and they have largely replaced pencil and paper as the theorist's main tool. Computers let theorists extend their studies of physical, chemical, and biological systems by solving difficult nonlinear problems in magnetohydrodynamics; atomic, molecular, and nuclear structure; fluid turbulence; shock hydrodynamics; and cosmological structure formation. Beyond such well-established aids to theorists and experimenters, the exponential growth of computer power is now launching the new field of computational science. Multidisciplinary computational teams are beginning to develop large-scale predictive simulations of highly complex technical problems. Large-scale codes have been created to simulate, with unprecedented fidelity, phenomena such as supernova explosions (see figures 1 and 2), inertialconfinement fusion, nuclear explosions (see the box on page 38), asteroid impacts (figure 3), and the effect of space weather on Earth's magnetosphere (figure 4). Computational simulation has the potential to join theory and experiment as a third powerful research methodology. Although, as figures 1-4 show, the new discipline is already yielding important and exciting results, it is also becoming all too clear that much of computational science is still troublingly immature. We point out three distinct challenges that computational science must meet if it is to fulfill its potential and take its place as a fully mature partner of theory and experiment: the performance challenge-producing high-performance computers, the programming challenge-programming for complex computers, and the prediction challenge-developing truly predictive complex application codes. The performance challenge requires that the exponential growth of computer performance continue, yielding ever larger memories and faster processing. The programming challenge involves the writing of codes that can efficiently exploit the capacities of the increasingly complex computers. The prediction challenge is to use all that computing power to provide answers reliable enough to form the basis for important decisions. The performance challenge is being met, at least for the next 10 years. Processor speed continues to increase, and massive parallelization is augmenting that speed, albeit at the cost of increasingly complex computer architectures. Massively parallel computers with thousands of processors are becoming widely available at relatively low cost, and larger ones are being developed. Much remains to be done to meet the programming challenge. But computer scientists are beginning to develop languages and software tools to facilitate programming for massively parallel computers.

Dynamically and Kinematically Consistent Global Ocean Circulation and Ice State Estimates

“…The history of fluid dynamics generally, and of complex model use in many fields, supports the inference that models unconstrained by data can and do often go wildly wrong (in the wider sense, see, e.g., May, 2004;Post and Votta, 2005). Readers will recognize the strong point of view taken by the present authors: that models unaccompanied by detailed, direct, comparisons with and constraints by data are best regarded as a kind of science novel with a mixture of truth and fiction.…”

Bridging the Chasm

“…Post and Votta [2005], for example refers to the need for a paradigm shift in this area. Indeed, advocates of software validation in scientific programming were making their arguments as early as the 1960s [Howden 1982;Naylor and Finger 1967].…”

TriBITS lifecycle model. Version 1.0, a lean/agile software lifecycle model for research-based computational science and engineering and applied mathematical software.

“…These tests must be developed at the beginning, and the internal structure of the software must be maintained at a higher quality using Agile/Emergent design and Continuous Refactoring [1]. Enhancing the quality of basic research software is needed just from the standpoint of creating credible research results that are suitable for publication in peer-reviewed journals [13,10].…”