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[Nar11] Formalisation et automatisation de YAO, générateur de code pour l'assimilation variationnelle de données.
Mémoire de Thèse : Soutenue le: 08 March 2011, pp. 147, pp.: Directeur: BADRAN FouadDirecteur: THIRIA Sylvie
Rapporteur 1: CHEVALLIER Frédéric
Rapporteur 2: SIARRY Patrick
Membre du jury: PICOULEAU Christophe
Membre du jury: BASTOUL Cédric
Membre du jury: HERLIN Isabelle
Membre du jury: MOULIN Cyril, : Formalisation et automatisation de YAO, générateur de code pour l'assimilation variationnelle de données.,
Mots clés: variational data assimilation, numerical model, adjoint model, automatic generation, automatic parallelization, shared memory, OpenMP
Résumé:
Variational data assimilation 4D-Var is a well-known technique used in geophysics, and in particular in meteorology and oceanography. This technique consists in estimating the control parameters of a direct numerical model, by minimizing a cost function which measures the misï¬t between the forecast values and some actual observations. The minimization, which is based on a gradient method, requires the computation of the adjoint model (product of the transpose Jacobian matrix and the derivative vector of the cost function at the observation points). In order to perform the 4DVar technique, we have to cope with complex program implementations, in particular concerning the adjoint model, the parallelization of the code and an efï¬cient memory management.
To address these difï¬culties and to facilitate the implementation of 4D-Var applications, LOCEAN is developing the YAO framework. YAO proposes to represent a direct model with a computation flow graph called modular graph. Modules depict computation units and edges between modules represent data transfer. Description directives proper to YAO allow a user to describe its direct model and to generate the modular graph associated to this model. YAO contains two core algorithms. The ï¬rst one is a forward propagation algorithm on the graph that computes the output of the numerical model ; the second one is a back propagation algorithm on the graph that computates the adjoint model. The main advantage of the YAO framework, is that the direct and adjoint model programming codes are automatically generated once the modular graph has been conceived by the user. Moreover, YAO allows to cope with many scenarios for running different data assimilation sessions.
This thesis introduces a computer science research on the YAO framework. In a ï¬rst step, we have formalized in a more general way the existing YAO speciï¬cations. Then algorithms allowing the automatization of some tasks have been proposed such as the automatic generation of an “optimal†computational ordering and the automatic parallelization of the generated code on shared memory architectures using OpenMP directives. This thesis permits to lay the foundations which, at medium term, will make of YAO a general and operational platform for data assimilation 4D-Var, allowing to process applications of high dimensions.
Equipe:
msdma
BibTeX
| @phdthesis { | |||
| Nar11, | |||
| title | = | "{Formalisation et automatisation de YAO, générateur de code pour l'assimilation variationnelle de données.}", | |
| author | = | "L. Nardi", | |
| year | = | 2011, | |
| pages | = | "147", | |
| address | = | "{CEDRIC Laboratory, Paris, France}", | |
| note | = | "{ Directeur: BADRAN Fouad Directeur: THIRIA Sylvie Rapporteur 1: CHEVALLIER Frédéric Rapporteur 2: SIARRY Patrick Membre du jury: PICOULEAU Christophe Membre du jury: BASTOUL Cédric Membre du jury: HERLIN Isabelle Membre du jury: MOULIN Cyril}", | |
| } |
