Team grand-large

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Section: Application Domains

End-User Tools for Computational Science and Engineering

Another Grand Large application domain is Large Scale Programming for Computational Science and Engineering. Two main approaches are proposed. First, we have to experiment and evaluate such programming. Second, we have to develop tools for end-users.

In addition to the classical supercomputing and the GRID computing based on virtual organization, the large scale P2P approach proposes new computing facilities for computational scientists and engineers. Thus, on one hand, it exists many applications, some of them are classical, such as Computational Fluid Dynamic or Quantum Physic ones, for example, and others are news and very strategic such as Nanotechnologies, which will have to use a lot of computing power for long period of time in the close future. On another hand, it emerges a new large scale programming paradigm for existing computers which can be accessible by scientific and engineer end-users for all classical application domains but also by new ones, such as some Non-Governmental Organisations. During a first period, many applications would be based on large simulations rather than classical implicit numerical methods, which are more difficult to adapt for such large problems and new programming paradigm. Nevertheless, we expected that more complex implicit methods would be adapted in the future for such programming. The potential number of peer and the planed evolution of network communications, especially multicast ones, would permit to contribute to solve some of the larger grand challenge scientific applications.

Simulations and large implicit methods would always have to compute linear algebra routines, which will be our first targeted numerical methods (we also remark that the powerful worldwide computing facilities are still rated using a linear algebra benchmark [ http://www.top500.org ]). We will especially first focus on divide-and-conquer and block-based matrix methods to solve dense problems and on iterative hybrid methods to solve sparse matrix problems. As these applications are utilized for many applications, it is possible to extrapolate the results to different scientific domains.

Many smart tools have to be developed to help the end-user to program such environments, using up-to-date component technologies and languages. At the actual present stage of maturity of this programming paradigm for scientific applications, the main goal is to experiment on large platforms, to evaluate and extrapolate performance, and to propose tools for the end-users; with respect to many parameters and under some specify hypothesis concerning scheduling strategies and multicast speeds [87] . We have to always replace the end-user at the center of this scientific programming. Then, we have to propose a framework to program P2P architectures which completely virtualized the P2P middleware and the heterogeneous hardware. Our approach is based, on one hand, on component programming and coordination languages, and on one another hand, to the development of an ASP, which may be dedicated to a targeted scientific domain. The conclusion would be a P2P scientific programming methodology based on experimentations and evaluation on an actual P2P development environment.


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