YAO: a generator of parallel code for variational data assimilation applications

Luigi Nardi 1, 2, * Fouad Badran 1 Pierre Fortin 3 Sylvie Thiria 2
* Corresponding author
1 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
2 MMSA - Modélisation et Méthodes Statistiques Avancées
LOCEAN - Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques
3 PEQUAN - Performance et Qualité des Algorithmes Numériques
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Variational data assimilation consists in estimating control parameters of a numerical model in order to minimize the misfit between the forecast values and the actual observations. The YAO framework is a code generator that facilitates, especially for the adjoint model, the writing and the generation of a variational data assimilation program for a given numerical application. In this paper we present how the modular graph specific to YAO enables the automatic and efficient parallelization of the generated code with OpenMP on shared memory architectures. Thanks to this modular graph we are also able to completely avoid the data race conditions (write/write conflicts). Performance tests with actual applications demonstrates good speedups on a multicore CPU.
Keywords : adjoint model numerical model OpenMP dependence graph data assimilation shared memory architectures automatic parallelization
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Luigi Nardi, Fouad Badran, Pierre Fortin, Sylvie Thiria. YAO: a generator of parallel code for variational data assimilation applications. 14th IEEE International Conference on High Performance Computing and Communications (HPCC-2012), Jun 2012, Liverpool, United Kingdom. pp.224-232, ⟨10.1109/HPCC.2012.38⟩. ⟨hal-00695513⟩

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