Section: Overall Objectives
The purpose of the ScAlApplix project is to analyze and solve scientific computation problems arising from complex research and industrial applications and involving scaling. These applications are characterized by the fact that they require enormous computing power, on the order of tens or hundreds of teraflops, and that they handle huge amounts of data. Solving these kinds of problems requires a multidisciplinary approach concerning both applied mathematics and computer science. In applied mathematics, it is essentially the field of numerical schemes that is concerned. In computer science, parallel computing and the design of high-performance codes to be executed on today's major computing platforms are concerned (parallel computers organized as a large network of SMP nodes, GRID platforms).
Through this approach, ScAlApplix intends to contribute to all steps in the line that goes from the design of new high-performance, more robust and more precise, numerical schemes to the optimized implementation of algorithms and codes for the simulation of physical (fluid mechanics, inert and reactive flows, multimaterial and multiphase flows), biological (molecular dynamics simulations) and environmental (host-parasite systems in population dynamics) phenomena that are by nature multiscale and multiphysics.
Another domain we are currently investigating is the development of distributed environments for coupling numerical codes and for steering interactively numerical simulations. The computational steering is an effort to make the typical simulation work-flow (modeling, computing, analyzing) more efficient, by providing on-line visualization and interactive steering over the on-going computational processes. On-line visualization appears very useful to monitor and detect possible errors in long-running applications, and interactive steering allows the researcher to alter simulation parameters on-the-fly and immediately receive feedback on their effects. Thus, scientists gain a better insight in the simulation regarding to the cause-effect relationship and can better grasp the complexity of the underlying models.