## Section: Overall Objectives

### 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.