7 avril pour jury

[these_gilles.git] / PRESENTATION / img / Gdr_ISIS_06_2012 / proposition_GdR1.tex

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\begin{document}
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% paper title
% can use linebreaks \\ within to get better formatting as desired
\title{Transposition GPU d'un algorithme de traitement d'images type 'snake'.}


% author names and affiliations
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\author{
\IEEEauthorblockN{Gilles Perrot$^1$, St\'{e}phane Domas$^1$, Rapha\"{e}l Couturier$^1$, Nicolas Bertaux$^2$}

\IEEEauthorblockA{$^1$Institut FEMTO-ST\\
Rue Engel Gros, 90000 Belfort, France.\\
prenom.nom@univ-fcomte.fr}

\IEEEauthorblockA{$^2$ Institut Fresnel, CNRS, Aix-Marseille Universit\'e, Ecole Centrale Marseille,\\
Campus de Saint-J\'er\^ome, 13013 Marseille, France.\\
nicolas.bertaux@ec-marseille.fr}
}


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\section*{\label{abstract}Résumé} 
En traitement d'images, une limite à l'utilisation d'un grand nombre d'algorithmes avancés demeure le temps de calcul de leurs implémentations.
Parallèlement, les gains obtenus aujourd'hui en puissance de calcul le sont essentiellement grâce l'emploi d'architectures parallèles de diverses natures.
Parmi ces solutions, les GPGPUs (processeurs graphiques à usage général) sont une réponse économique et performante pour l'implémentation de certaines classes d'algorithmes.
Toutefois, leur architecture très spécifique ainsi que l'interaction avec le processeur hôte ne permettent pas de garantir, dans tous les cas, une transposition simple et efficace. À tel point qu'il n'est pas rare de voir des implémentions GPU moins performantes que l'implémentation CPU de référence.
Nous proposons d'illustrer ces aspects en s'appuyant sur l'exemple d'un algorithme de segmentation par contour actif, orienté régions (snake).
Après avoir rappelé brièvement le principe du \textit{snake} étudié, nous détaillerons la méthode employée pour paralléliser cet algorithme sur GPU, en nous focalisant sur certains aspects génériques et symptomatiques. Nous évoquerons en particulier, en donnant autant que possible des extraits de code : 
la représentation des données liée au niveau de parallélisme, la génération d'images intégrales et les étapes de réduction, l'optimisation de la grille de calcul et la notion d'occupation (occupancy).  
\end{document}