From: zulu Date: Fri, 20 Dec 2013 07:37:54 +0000 (+0100) Subject: 20 déc X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/these_gilles.git/commitdiff_plain/6933b340c2bae6a25a33f2bb9a26c91bda0c171c?ds=inline 20 déc --- diff --git a/THESE/Chapters/chapter1/conclusion.tex b/THESE/Chapters/chapter1/conclusion.tex index 1d0af6b..ff8cdbc 100644 --- a/THESE/Chapters/chapter1/conclusion.tex +++ b/THESE/Chapters/chapter1/conclusion.tex @@ -1,18 +1,18 @@ Les travaux présentés dans ce manuscrit partagent le même cadre et les mêmes objectifs, à savoir effectuer des opérations de filtrage ou de segmentation sur des images bruitées en exploitant au mieux les capacités de traitement des GPUs. Comme le laissait entrevoir notre intuition première, ces traitements présentent des propriétés de parallélisme très diverses, conduisant à des implémentations plus ou moins efficaces sur GPU. -Certains algorithmes, comme le filtrage contraint par les lignes de niveaux, ont pu voir leur objectif opérationnel conservé en adaptant les modèles aux caractéristiques particulières des GPU, ce qui a permis d'atteindre des niveaux de performance élevés. Pour d'autres opérations plus classiques, comme les filtrages médian ou de convolution, la problématique est autre, puisqu'il s'agit d'effectuer des traitements de référence ayant une définition mathématique clairement établie. Toute implémentation efficace requiert la conception de structures garantissant l'exactitude du calcul tout en optimisant l'utilisation du GPU. Nous avons, dans ce domaine, contribué significativement à l'amélioration des performances, en approchant du maximum permis par nos architectures de test. +Certains algorithmes, comme le filtrage contraint par les lignes de niveaux, ont pu voir leur objectif opérationnel conservé en adaptant les modèles aux caractéristiques particulières des GPUs, ce qui a permis d'atteindre des niveaux de performance élevés. Pour d'autres opérations plus classiques, comme les filtrages médian ou de convolution, la problématique est autre, puisqu'il s'agit d'effectuer des traitements de référence ayant une définition mathématique clairement établie. Toute implémentation efficace requiert la conception de structures garantissant l'exactitude du calcul tout en optimisant l'utilisation du GPU. Nous avons, dans ce domaine, contribué significativement à l'amélioration des performances, en approchant du maximum permis par nos architectures de test. -Dans tous les cas, les implémentation présentées ici sont le fruit d'une recherche approfondie et quasi-exhaustive de la meilleure utilisation des différents types de mémoire disponibles sur GPU. Nos travaux mettent en évidence des résultats qui ébranlent le paradigme de la mémoire partagée comme unique chemin vers la performance. En effet, dans de nombreux cas, l'utilisation de la mémoire partagée s'avère pénalisante et le recours aux registres internes des c\oe urs de calcul se révèle bien plus efficace. +Dans tous les cas, les implémentations présentées ici sont le fruit d'une recherche approfondie et quasi-exhaustive de la meilleure utilisation des différents types de mémoire disponibles sur GPU. Nos travaux mettent en évidence des résultats qui ébranlent le paradigme de la mémoire partagée comme unique chemin vers la performance. En effet, dans de nombreux cas, l'utilisation de la mémoire partagée s'avère pénalisante et le recours aux registres internes des c\oe urs de calcul se révèle bien plus efficace. L'optimisation des calculs n'est de surcroît pas le seul aspect dont l'implémentation doive être soignée. Nous avons ainsi optimisé les temps de transfert entre CPU et GPU pris en compte dans nos calculs de performances, contrairement à la plupart des articles de référence qui \og omettent\fg{} de les intégrer à leurs mesures. L'algorithme de type \textit{snake} s'est avéré particulièrement délicat à implémenter efficacement sur GPU, la version parallèle ne surpassant la version séquentielle que pour des images de très grande taille. Les motifs d'accès à la mémoire demeurent beaucoup trop irréguliers pour être performants et conduisent à exécuter des grilles de calcul creuses ne permettant pas de masquer efficacement les latences. Toutefois, les modèles de GPU les plus récents, basés sur l'architecture Kepler, permettent d'entrevoir la possibilité de minimiser ce problème en recourant à deux de leurs nouvelles fonctionnalités : la faculté d'exécuter simultanément plusieurs kernels et le parallélisme dynamique. -Nos travaux remettent également en cause l'idée trop souvent reçue que le simple portage d'applications séquentielles sur GPU permet systématiquement d'en décupler les performances. Certains algorithmes, tels le snake, font par exemple appel à des opérations de réduction sur des grilles de calcul creuses, et sont clairement peu adaptées aux GPUs. De plus, les algorithmes que l'on parvient à implémenter efficacement ne le sont qu'au prix d'optimisations particulièrement ardues. +Nos travaux remettent également en cause l'idée trop souvent reçue que le simple portage d'applications séquentielles sur GPU permet systématiquement d'en décupler les performances. Certains algorithmes, tels le snake, font par exemple appel à des opérations de réduction sur des grilles de calcul creuses, et sont clairement peu adaptés aux GPUs. De plus, les algorithmes que l'on parvient à implémenter efficacement ne le sont qu'au prix d'optimisations particulièrement ardues. Beaucoup d'autres traitements sont susceptibles de bénéficier des performances toujours en hausse des cartes graphiques modernes. Certains n'ont pas encore été portés sur ces plateformes, d'autres l'ont été, mais n'atteignent pas toujours les performances attendues. Sur la base des techniques et savoir-faire que nous avons pu développer durant ces années de thèse, il est permis de penser que nous pourrons à l'avenir contribuer à améliorer significativement cet état de fait, avec des solutions adaptées tant aux évolutions des matériels qu'aux diverses problématiques scientifiques. À court terme, nous envisageons d'appliquer les techniques exposées ici aux algorithmes de traitement d'image qui font référence en termes de qualité, comme \textit{BM3D} ou \textit{level-sets}, qui nous paraissent susceptibles d'en tirer parti. -Parallèlement, les résultats obtenus concernant la convolution séparable montrent que nos méthodes apportent aussi un gain de performances important sur les opérations 1-D, ce qui nous permettra d'étendre leur champ d'application à l'ensemble des signaux mono-dimensionnels comme les signaux audio. +Outre les performances obtenues dans le traitement des images 2-D, les résultats obtenus concernant la convolution séparable montrent que nos méthodes apportent aussi un gain de performances important sur les opérations 1-D, ce qui nous permettra d'étendre leur champ d'application à l'ensemble des signaux mono-dimensionnels comme les signaux audio. Enfin, le procédé d'exploitation des recouvrements de voisinages, d'autant plus efficace que ces recouvrements sont importants, ouvre des perspectives très prometteuses dans le traitement des images 3-D. Nous envisageons d'étendre nos recherches à ce domaine et à ses nombreuses applications, notamment l'interprétation des données issues de l'imagerie médicale. diff --git a/THESE/Chapters/chapter5/chapter5.tex b/THESE/Chapters/chapter5/chapter5.tex index be63700..a5581b8 100644 --- a/THESE/Chapters/chapter5/chapter5.tex +++ b/THESE/Chapters/chapter5/chapter5.tex @@ -6,7 +6,6 @@ Les performances annoncées pour des fenêtres de petite taille comme 3$\times$3 Un rapide prototypage a conforté cette idée et nous a conduit à chercher plus avant une technique d'implémentation du filtre médian qui exploite pleinement les capacités de nos GPU. \section{Les transferts de données} - Le chapitre \ref{ch-GPU}, présentant l'architecture et les caractéristiques principales des GPUs, donne également la liste et les spécificités des types de mémoire accessibles par un kernel. Lorsqu'il s'agit de stocker des volumes importants de données, comme les images d'entrée et de sortie, les alternatives sont assez limitées. En effet, le seul espace mémoire suffisamment important est celui la mémoire dite globale, malheureusement la plus lente. On dispose cependant de plusieurs modes pour y accéder, comme la déclaration de textures, qui offre un mécanisme de cache 2D permettant d'augmenter assez nettement les débits en lecture dans le cas d'accès au voisinage d'une donnée. Dans le cadre de nos travaux, cette mémorisation sous forme de texture s'est montrée la plus performante pour les images d'entrée. @@ -80,7 +79,6 @@ De ce point de vue, l'architecture Fermi, et en particulier le modèle C2070, ne \subsection{La sélection de la valeur médiane} - Dans le cas des filtres médians à petite fenêtre, on peut envisager d'attribuer un registre par valeur à trier. Dans ce cas, un médian 3$\times$3 emploiera 9 registres par thread, et cette méthode pourra théoriquement s'appliquer jusqu'au médian 7$\times$7 sur C2070 et 11$\times$11 sur C1060. Comme la recherche de performance impose de rationaliser l'utilisation des registres, nous nous sommes orientés vers l'algorithme dit \textit{forgetful selection} (sélection par oubli) qui évite d'avoir recours à cette cardinalité de \og un registre pour un pixel\fg{} de la fenêtre (\cite{medianggems5}). @@ -156,7 +154,7 @@ L'ensemble des choix que nous venons de décrire et qui ont présidé à l'élab Les valeurs présentées dans les tableaux \ref{tab-median-coutcpy}, \ref{tab-median-chronos} et la figure \ref{fig-median-comp} sont obtenues par moyennage du chronométrage de 1000 exécutions du même kernel, développé en variantes 8 et 16 bits de profondeurs de niveau de gris. -La première analyse que nous pouvons en faire est la pertinence des choix faits quant aux transferts de données, qui représentent entre 13\% et 82\% du temps total d'exécution des configurations testées. +L'analyse que nous pouvons tirer du tableau \ref{tab-median-coutcpy} est la pertinence des choix relatifs aux transferts de données, qui représentent entre 13\% et 82\% du temps total d'exécution des configurations testées. \begin{table}[ht] \renewcommand{\arraystretch}{1.5} diff --git a/THESE/these.log b/THESE/these.log index d259448..29ef7a4 100644 --- a/THESE/these.log +++ b/THESE/these.log @@ -1,4 +1,4 @@ -This is pdfTeX, Version 3.1415926-1.40.10 (TeX Live 2009/Debian) (format=pdflatex 2012.12.6) 19 DEC 2013 09:48 +This is pdfTeX, Version 3.1415926-1.40.10 (TeX Live 2009/Debian) (format=pdflatex 2012.12.6) 19 DEC 2013 15:18 entering extended mode %&-line parsing enabled. **these.tex @@ -3074,7 +3074,7 @@ Chapitre 8. ] LaTeX Font Info: Font shape `T1/phv/bx/n' in size <9> not available -(Font) Font shape `T1/phv/b/n' tried instead on input line 19. +(Font) Font shape `T1/phv/b/n' tried instead on input line 18. File: kernLeft.png Graphic file (type png) @@ -3098,7 +3098,7 @@ File: Chapters/chapter5/img/median5_overlap.png Graphic file (type png) [108 <./Chapters/chapter5/img/m edian5_overlap.png>] Package hyperref Info: bookmark level for unknown lstlisting defaults to 0 on i -nput line 145. +nput line 143. (./Chapters/chapter5/code/median3-2pix.cu) File: Chapters/chapter5/img/comp512.png Graphic file (type png) @@ -3115,11 +3115,11 @@ Chapitre 9. ] (./Chapters/chapter6/code/convoGene3Reg8.cu) [114] [115] - + File: Chapters/chapter6/img/convoOverlap1.png Graphic file (type png) - + File: Chapters/chapter6/img/convoOverlap2.png Graphic file (type png) [116 <./Chapters/chapter6/img/con @@ -3141,11 +3141,11 @@ LaTeX Warning: `h' float specifier changed to `ht'. 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1255 0 R (equation.6.2.9) 1256 0 R (equation.7.2.1) 1585 0 R (equation.7.2.2) 1586 0 R (equation.7.2.3) 1587 0 R] /Limits [(equation.6.2.7) (equation.7.2.3)] >> endobj -2883 0 obj << +2884 0 obj << /Names [(equation.7.2.4) 1588 0 R (equation.7.2.5) 1589 0 R (equation.7.2.6) 1601 0 R (equation.7.2.7) 1611 0 R (equation.7.2.8) 1602 0 R (equation.7.3.10) 1709 0 R] /Limits [(equation.7.2.4) (equation.7.3.10)] >> endobj -2884 0 obj << +2885 0 obj << /Names [(equation.7.3.11) 1823 0 R (equation.7.3.9) 1708 0 R (equation.7.5.12) 1874 0 R (equation.7.5.13) 1875 0 R (equation.7.5.14) 1876 0 R (equation.7.5.15) 1878 0 R] /Limits [(equation.7.3.11) (equation.7.5.15)] >> endobj -2885 0 obj << +2886 0 obj << /Names [(equation.7.5.16) 1914 0 R (equation.7.5.17) 1915 0 R (equation.7.5.18) 1916 0 R (equation.7.5.19) 1917 0 R (figure.2.1) 575 0 R (figure.2.2) 578 0 R] /Limits [(equation.7.5.16) (figure.2.2)] >> endobj -2886 0 obj << +2887 0 obj << /Names [(figure.2.3) 594 0 R (figure.2.4) 611 0 R (figure.4.1) 688 0 R (figure.4.2) 710 0 R (figure.4.3) 722 0 R (figure.4.4) 755 0 R] /Limits [(figure.2.3) (figure.4.4)] >> endobj -2887 0 obj << +2888 0 obj << /Names [(figure.4.5) 797 0 R (figure.4.6) 801 0 R (figure.4.7) 802 0 R (figure.4.8) 831 0 R (figure.4.9) 850 0 R (figure.5.1) 899 0 R] /Limits [(figure.4.5) (figure.5.1)] >> endobj -2888 0 obj << +2889 0 obj << /Names [(figure.5.10) 1173 0 R (figure.5.11) 1200 0 R (figure.5.2) 946 0 R (figure.5.3) 976 0 R (figure.5.4) 998 0 R (figure.5.5) 1016 0 R] /Limits [(figure.5.10) (figure.5.5)] >> endobj -2889 0 obj << +2890 0 obj << /Names [(figure.5.6) 1085 0 R (figure.5.7) 1119 0 R (figure.5.8) 1121 0 R (figure.5.9) 1170 0 R (figure.6.1) 1262 0 R (figure.6.10) 1490 0 R] /Limits [(figure.5.6) (figure.6.10)] >> endobj -2890 0 obj << +2891 0 obj << /Names [(figure.6.11) 1509 0 R (figure.6.12) 1531 0 R (figure.6.2) 1356 0 R (figure.6.3) 1357 0 R (figure.6.4) 1385 0 R (figure.6.5) 1386 0 R] /Limits [(figure.6.11) (figure.6.5)] >> endobj -2891 0 obj << +2892 0 obj << /Names [(figure.6.6) 1403 0 R (figure.6.7) 1417 0 R (figure.6.8) 1458 0 R (figure.6.9) 1471 0 R (figure.7.1) 1575 0 R (figure.7.10) 1831 0 R] /Limits [(figure.6.6) (figure.7.10)] >> endobj -2892 0 obj << +2893 0 obj << /Names [(figure.7.11) 1824 0 R (figure.7.12) 1841 0 R (figure.7.13) 1867 0 R (figure.7.14) 1958 0 R (figure.7.15) 1971 0 R (figure.7.2) 1576 0 R] /Limits [(figure.7.11) (figure.7.2)] >> endobj -2893 0 obj << +2894 0 obj << /Names [(figure.7.3) 1599 0 R (figure.7.4) 1651 0 R (figure.7.5) 1685 0 R (figure.7.6) 1686 0 R (figure.7.7) 1688 0 R (figure.7.8) 1725 0 R] /Limits [(figure.7.3) (figure.7.8)] >> endobj -2894 0 obj << -/Names [(figure.7.9) 1752 0 R (figure.8.1) 2057 0 R (figure.8.2) 2074 0 R (figure.8.3) 2083 0 R (figure.8.4) 2153 0 R (figure.9.1) 2254 0 R] +2895 0 obj << +/Names [(figure.7.9) 1752 0 R (figure.8.1) 2057 0 R (figure.8.2) 2074 0 R (figure.8.3) 2083 0 R (figure.8.4) 2153 0 R (figure.9.1) 2255 0 R] /Limits [(figure.7.9) (figure.9.1)] >> endobj -2895 0 obj << -/Names [(figure.9.2) 2569 0 R (lstlisting.8.1) 2094 0 R (lstlisting.9.1) 2202 0 R (lstlisting.9.2) 2266 0 R (lstlisting.9.3) 2339 0 R (lstlisting.9.4) 2401 0 R] +2896 0 obj << +/Names [(figure.9.2) 2570 0 R (lstlisting.8.1) 2094 0 R (lstlisting.9.1) 2203 0 R (lstlisting.9.2) 2267 0 R (lstlisting.9.3) 2340 0 R (lstlisting.9.4) 2402 0 R] /Limits [(figure.9.2) (lstlisting.9.4)] >> endobj -2896 0 obj << -/Names [(lstlisting.9.5) 2478 0 R (lstnumber.8.1.1) 2095 0 R (lstnumber.8.1.10) 2104 0 R (lstnumber.8.1.11) 2105 0 R (lstnumber.8.1.12) 2106 0 R (lstnumber.8.1.13) 2107 0 R] +2897 0 obj << +/Names [(lstlisting.9.5) 2479 0 R (lstnumber.8.1.1) 2095 0 R (lstnumber.8.1.10) 2104 0 R (lstnumber.8.1.11) 2105 0 R (lstnumber.8.1.12) 2106 0 R (lstnumber.8.1.13) 2107 0 R] /Limits [(lstlisting.9.5) (lstnumber.8.1.13)] >> endobj -2897 0 obj << +2898 0 obj << /Names [(lstnumber.8.1.14) 2108 0 R (lstnumber.8.1.15) 2109 0 R (lstnumber.8.1.16) 2110 0 R (lstnumber.8.1.17) 2111 0 R (lstnumber.8.1.18) 2112 0 R (lstnumber.8.1.19) 2113 0 R] /Limits [(lstnumber.8.1.14) (lstnumber.8.1.19)] >> endobj -2898 0 obj << +2899 0 obj << /Names [(lstnumber.8.1.2) 2096 0 R (lstnumber.8.1.20) 2114 0 R (lstnumber.8.1.21) 2115 0 R (lstnumber.8.1.22) 2116 0 R (lstnumber.8.1.23) 2117 0 R (lstnumber.8.1.24) 2118 0 R] /Limits [(lstnumber.8.1.2) (lstnumber.8.1.24)] >> endobj -2899 0 obj << +2900 0 obj << /Names [(lstnumber.8.1.25) 2119 0 R (lstnumber.8.1.26) 2120 0 R (lstnumber.8.1.27) 2121 0 R (lstnumber.8.1.28) 2122 0 R (lstnumber.8.1.29) 2123 0 R (lstnumber.8.1.3) 2097 0 R] /Limits [(lstnumber.8.1.25) (lstnumber.8.1.3)] >> endobj -2900 0 obj << +2901 0 obj << /Names [(lstnumber.8.1.30) 2124 0 R (lstnumber.8.1.31) 2125 0 R (lstnumber.8.1.32) 2126 0 R (lstnumber.8.1.33) 2127 0 R (lstnumber.8.1.34) 2128 0 R (lstnumber.8.1.35) 2129 0 R] /Limits [(lstnumber.8.1.30) (lstnumber.8.1.35)] >> endobj -2901 0 obj << +2902 0 obj << /Names [(lstnumber.8.1.36) 2130 0 R (lstnumber.8.1.37) 2131 0 R (lstnumber.8.1.38) 2132 0 R (lstnumber.8.1.39) 2133 0 R (lstnumber.8.1.4) 2098 0 R (lstnumber.8.1.40) 2134 0 R] /Limits [(lstnumber.8.1.36) (lstnumber.8.1.40)] >> endobj -2902 0 obj << +2903 0 obj << /Names [(lstnumber.8.1.41) 2135 0 R (lstnumber.8.1.42) 2136 0 R (lstnumber.8.1.43) 2137 0 R (lstnumber.8.1.44) 2138 0 R (lstnumber.8.1.45) 2139 0 R (lstnumber.8.1.46) 2140 0 R] /Limits [(lstnumber.8.1.41) (lstnumber.8.1.46)] >> endobj -2903 0 obj << +2904 0 obj << /Names [(lstnumber.8.1.47) 2141 0 R (lstnumber.8.1.48) 2142 0 R (lstnumber.8.1.49) 2143 0 R (lstnumber.8.1.5) 2099 0 R (lstnumber.8.1.50) 2144 0 R (lstnumber.8.1.51) 2145 0 R] /Limits [(lstnumber.8.1.47) (lstnumber.8.1.51)] >> endobj -2904 0 obj << +2905 0 obj << /Names [(lstnumber.8.1.52) 2146 0 R (lstnumber.8.1.53) 2147 0 R (lstnumber.8.1.54) 2148 0 R (lstnumber.8.1.55) 2149 0 R (lstnumber.8.1.6) 2100 0 R (lstnumber.8.1.7) 2101 0 R] /Limits [(lstnumber.8.1.52) (lstnumber.8.1.7)] >> endobj -2905 0 obj << -/Names [(lstnumber.8.1.8) 2102 0 R (lstnumber.8.1.9) 2103 0 R (lstnumber.9.1.1) 2203 0 R (lstnumber.9.1.10) 2212 0 R (lstnumber.9.1.11) 2213 0 R (lstnumber.9.1.12) 2214 0 R] +2906 0 obj << +/Names [(lstnumber.8.1.8) 2102 0 R (lstnumber.8.1.9) 2103 0 R (lstnumber.9.1.1) 2204 0 R (lstnumber.9.1.10) 2213 0 R (lstnumber.9.1.11) 2214 0 R (lstnumber.9.1.12) 2215 0 R] /Limits [(lstnumber.8.1.8) (lstnumber.9.1.12)] >> endobj -2906 0 obj << -/Names [(lstnumber.9.1.13) 2215 0 R (lstnumber.9.1.14) 2216 0 R (lstnumber.9.1.15) 2217 0 R (lstnumber.9.1.16) 2218 0 R (lstnumber.9.1.17) 2219 0 R (lstnumber.9.1.18) 2220 0 R] +2907 0 obj << +/Names [(lstnumber.9.1.13) 2216 0 R (lstnumber.9.1.14) 2217 0 R (lstnumber.9.1.15) 2218 0 R (lstnumber.9.1.16) 2219 0 R (lstnumber.9.1.17) 2220 0 R (lstnumber.9.1.18) 2221 0 R] /Limits [(lstnumber.9.1.13) (lstnumber.9.1.18)] >> endobj -2907 0 obj << -/Names [(lstnumber.9.1.19) 2221 0 R (lstnumber.9.1.2) 2204 0 R (lstnumber.9.1.20) 2222 0 R (lstnumber.9.1.21) 2223 0 R (lstnumber.9.1.22) 2224 0 R (lstnumber.9.1.23) 2225 0 R] +2908 0 obj << +/Names [(lstnumber.9.1.19) 2222 0 R (lstnumber.9.1.2) 2205 0 R (lstnumber.9.1.20) 2223 0 R (lstnumber.9.1.21) 2224 0 R (lstnumber.9.1.22) 2225 0 R (lstnumber.9.1.23) 2226 0 R] /Limits [(lstnumber.9.1.19) (lstnumber.9.1.23)] >> endobj -2908 0 obj << -/Names [(lstnumber.9.1.24) 2226 0 R (lstnumber.9.1.25) 2227 0 R (lstnumber.9.1.26) 2228 0 R (lstnumber.9.1.27) 2229 0 R (lstnumber.9.1.28) 2230 0 R (lstnumber.9.1.29) 2231 0 R] +2909 0 obj << +/Names [(lstnumber.9.1.24) 2227 0 R (lstnumber.9.1.25) 2228 0 R (lstnumber.9.1.26) 2229 0 R (lstnumber.9.1.27) 2230 0 R (lstnumber.9.1.28) 2231 0 R (lstnumber.9.1.29) 2232 0 R] /Limits [(lstnumber.9.1.24) (lstnumber.9.1.29)] >> endobj -2909 0 obj << -/Names [(lstnumber.9.1.3) 2205 0 R (lstnumber.9.1.30) 2232 0 R (lstnumber.9.1.31) 2233 0 R (lstnumber.9.1.4) 2206 0 R (lstnumber.9.1.5) 2207 0 R (lstnumber.9.1.6) 2208 0 R] +2910 0 obj << +/Names [(lstnumber.9.1.3) 2206 0 R (lstnumber.9.1.30) 2233 0 R (lstnumber.9.1.31) 2234 0 R (lstnumber.9.1.4) 2207 0 R (lstnumber.9.1.5) 2208 0 R (lstnumber.9.1.6) 2209 0 R] /Limits [(lstnumber.9.1.3) (lstnumber.9.1.6)] >> endobj -2910 0 obj << -/Names [(lstnumber.9.1.7) 2209 0 R (lstnumber.9.1.8) 2210 0 R (lstnumber.9.1.9) 2211 0 R (lstnumber.9.2.1) 2267 0 R (lstnumber.9.2.10) 2280 0 R (lstnumber.9.2.11) 2281 0 R] +2911 0 obj << +/Names [(lstnumber.9.1.7) 2210 0 R (lstnumber.9.1.8) 2211 0 R (lstnumber.9.1.9) 2212 0 R (lstnumber.9.2.1) 2268 0 R (lstnumber.9.2.10) 2281 0 R (lstnumber.9.2.11) 2282 0 R] /Limits [(lstnumber.9.1.7) (lstnumber.9.2.11)] >> endobj -2911 0 obj << -/Names [(lstnumber.9.2.12) 2282 0 R (lstnumber.9.2.13) 2283 0 R (lstnumber.9.2.14) 2284 0 R (lstnumber.9.2.15) 2285 0 R (lstnumber.9.2.16) 2286 0 R (lstnumber.9.2.17) 2287 0 R] +2912 0 obj << +/Names [(lstnumber.9.2.12) 2283 0 R (lstnumber.9.2.13) 2284 0 R (lstnumber.9.2.14) 2285 0 R (lstnumber.9.2.15) 2286 0 R (lstnumber.9.2.16) 2287 0 R (lstnumber.9.2.17) 2288 0 R] /Limits [(lstnumber.9.2.12) (lstnumber.9.2.17)] >> endobj -2912 0 obj << -/Names [(lstnumber.9.2.18) 2288 0 R (lstnumber.9.2.19) 2289 0 R (lstnumber.9.2.2) 2272 0 R (lstnumber.9.2.20) 2290 0 R (lstnumber.9.2.21) 2291 0 R (lstnumber.9.2.22) 2292 0 R] +2913 0 obj << +/Names [(lstnumber.9.2.18) 2289 0 R (lstnumber.9.2.19) 2290 0 R (lstnumber.9.2.2) 2273 0 R (lstnumber.9.2.20) 2291 0 R (lstnumber.9.2.21) 2292 0 R (lstnumber.9.2.22) 2293 0 R] /Limits [(lstnumber.9.2.18) (lstnumber.9.2.22)] >> endobj -2913 0 obj << -/Names [(lstnumber.9.2.23) 2293 0 R (lstnumber.9.2.24) 2294 0 R (lstnumber.9.2.25) 2295 0 R (lstnumber.9.2.26) 2296 0 R (lstnumber.9.2.27) 2297 0 R (lstnumber.9.2.28) 2298 0 R] +2914 0 obj << +/Names [(lstnumber.9.2.23) 2294 0 R (lstnumber.9.2.24) 2295 0 R (lstnumber.9.2.25) 2296 0 R (lstnumber.9.2.26) 2297 0 R (lstnumber.9.2.27) 2298 0 R (lstnumber.9.2.28) 2299 0 R] /Limits [(lstnumber.9.2.23) (lstnumber.9.2.28)] >> endobj -2914 0 obj << -/Names [(lstnumber.9.2.29) 2299 0 R (lstnumber.9.2.3) 2273 0 R (lstnumber.9.2.30) 2300 0 R (lstnumber.9.2.31) 2301 0 R (lstnumber.9.2.32) 2302 0 R (lstnumber.9.2.33) 2303 0 R] +2915 0 obj << +/Names [(lstnumber.9.2.29) 2300 0 R (lstnumber.9.2.3) 2274 0 R (lstnumber.9.2.30) 2301 0 R (lstnumber.9.2.31) 2302 0 R (lstnumber.9.2.32) 2303 0 R (lstnumber.9.2.33) 2304 0 R] /Limits [(lstnumber.9.2.29) (lstnumber.9.2.33)] >> endobj -2915 0 obj << -/Names [(lstnumber.9.2.34) 2304 0 R (lstnumber.9.2.35) 2305 0 R (lstnumber.9.2.36) 2306 0 R (lstnumber.9.2.37) 2307 0 R (lstnumber.9.2.38) 2308 0 R (lstnumber.9.2.39) 2309 0 R] +2916 0 obj << +/Names [(lstnumber.9.2.34) 2305 0 R (lstnumber.9.2.35) 2306 0 R (lstnumber.9.2.36) 2307 0 R (lstnumber.9.2.37) 2308 0 R (lstnumber.9.2.38) 2309 0 R (lstnumber.9.2.39) 2310 0 R] /Limits [(lstnumber.9.2.34) (lstnumber.9.2.39)] >> endobj -2916 0 obj << -/Names [(lstnumber.9.2.4) 2274 0 R (lstnumber.9.2.40) 2310 0 R (lstnumber.9.2.41) 2311 0 R (lstnumber.9.2.42) 2312 0 R (lstnumber.9.2.43) 2313 0 R (lstnumber.9.2.44) 2314 0 R] +2917 0 obj << +/Names [(lstnumber.9.2.4) 2275 0 R (lstnumber.9.2.40) 2311 0 R (lstnumber.9.2.41) 2312 0 R (lstnumber.9.2.42) 2313 0 R (lstnumber.9.2.43) 2314 0 R (lstnumber.9.2.44) 2315 0 R] /Limits [(lstnumber.9.2.4) (lstnumber.9.2.44)] >> endobj -2917 0 obj << -/Names [(lstnumber.9.2.45) 2315 0 R (lstnumber.9.2.46) 2316 0 R (lstnumber.9.2.47) 2317 0 R (lstnumber.9.2.48) 2318 0 R (lstnumber.9.2.49) 2319 0 R (lstnumber.9.2.5) 2275 0 R] +2918 0 obj << +/Names [(lstnumber.9.2.45) 2316 0 R (lstnumber.9.2.46) 2317 0 R (lstnumber.9.2.47) 2318 0 R (lstnumber.9.2.48) 2319 0 R (lstnumber.9.2.49) 2320 0 R (lstnumber.9.2.5) 2276 0 R] /Limits [(lstnumber.9.2.45) (lstnumber.9.2.5)] >> endobj -2918 0 obj << -/Names [(lstnumber.9.2.50) 2320 0 R (lstnumber.9.2.51) 2321 0 R (lstnumber.9.2.52) 2322 0 R (lstnumber.9.2.53) 2323 0 R (lstnumber.9.2.54) 2324 0 R (lstnumber.9.2.55) 2325 0 R] +2919 0 obj << +/Names [(lstnumber.9.2.50) 2321 0 R (lstnumber.9.2.51) 2322 0 R (lstnumber.9.2.52) 2323 0 R (lstnumber.9.2.53) 2324 0 R (lstnumber.9.2.54) 2325 0 R (lstnumber.9.2.55) 2326 0 R] /Limits [(lstnumber.9.2.50) (lstnumber.9.2.55)] >> endobj -2919 0 obj << -/Names [(lstnumber.9.2.56) 2326 0 R (lstnumber.9.2.57) 2327 0 R (lstnumber.9.2.58) 2328 0 R (lstnumber.9.2.59) 2329 0 R (lstnumber.9.2.6) 2276 0 R (lstnumber.9.2.60) 2330 0 R] +2920 0 obj << +/Names [(lstnumber.9.2.56) 2327 0 R (lstnumber.9.2.57) 2328 0 R (lstnumber.9.2.58) 2329 0 R (lstnumber.9.2.59) 2330 0 R (lstnumber.9.2.6) 2277 0 R (lstnumber.9.2.60) 2331 0 R] /Limits [(lstnumber.9.2.56) (lstnumber.9.2.60)] >> endobj -2920 0 obj << -/Names [(lstnumber.9.2.61) 2331 0 R (lstnumber.9.2.62) 2332 0 R (lstnumber.9.2.7) 2277 0 R (lstnumber.9.2.8) 2278 0 R (lstnumber.9.2.9) 2279 0 R (lstnumber.9.3.1) 2340 0 R] +2921 0 obj << +/Names [(lstnumber.9.2.61) 2332 0 R (lstnumber.9.2.62) 2333 0 R (lstnumber.9.2.7) 2278 0 R (lstnumber.9.2.8) 2279 0 R (lstnumber.9.2.9) 2280 0 R (lstnumber.9.3.1) 2341 0 R] /Limits [(lstnumber.9.2.61) (lstnumber.9.3.1)] >> endobj -2921 0 obj << -/Names [(lstnumber.9.3.10) 2353 0 R (lstnumber.9.3.11) 2354 0 R (lstnumber.9.3.12) 2355 0 R (lstnumber.9.3.13) 2356 0 R (lstnumber.9.3.14) 2357 0 R (lstnumber.9.3.15) 2358 0 R] +2922 0 obj << +/Names [(lstnumber.9.3.10) 2354 0 R (lstnumber.9.3.11) 2355 0 R (lstnumber.9.3.12) 2356 0 R (lstnumber.9.3.13) 2357 0 R (lstnumber.9.3.14) 2358 0 R (lstnumber.9.3.15) 2359 0 R] /Limits [(lstnumber.9.3.10) (lstnumber.9.3.15)] >> endobj -2922 0 obj << -/Names [(lstnumber.9.3.16) 2359 0 R (lstnumber.9.3.17) 2360 0 R (lstnumber.9.3.18) 2361 0 R (lstnumber.9.3.19) 2362 0 R (lstnumber.9.3.2) 2341 0 R (lstnumber.9.3.20) 2363 0 R] +2923 0 obj << +/Names [(lstnumber.9.3.16) 2360 0 R (lstnumber.9.3.17) 2361 0 R (lstnumber.9.3.18) 2362 0 R (lstnumber.9.3.19) 2363 0 R (lstnumber.9.3.2) 2342 0 R (lstnumber.9.3.20) 2364 0 R] /Limits [(lstnumber.9.3.16) (lstnumber.9.3.20)] >> endobj -2923 0 obj << -/Names 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677 0 R] /Limits [(page.21) (page.26)] >> endobj -2961 0 obj << +2962 0 obj << /Names [(page.27) 687 0 R (page.28) 705 0 R (page.29) 715 0 R (page.3) 399 0 R (page.30) 751 0 R (page.31) 767 0 R] /Limits [(page.27) (page.31)] >> endobj -2962 0 obj << +2963 0 obj << /Names [(page.32) 793 0 R (page.33) 808 0 R (page.34) 828 0 R (page.35) 846 0 R (page.36) 859 0 R (page.37) 863 0 R] /Limits [(page.32) (page.37)] >> endobj -2963 0 obj << +2964 0 obj << /Names [(page.38) 870 0 R (page.39) 876 0 R (page.4) 438 0 R (page.40) 896 0 R (page.41) 908 0 R (page.42) 939 0 R] /Limits [(page.38) (page.42)] >> endobj -2964 0 obj << +2965 0 obj << /Names [(page.43) 966 0 R (page.44) 989 0 R (page.45) 1014 0 R (page.46) 1029 0 R (page.47) 1044 0 R (page.48) 1061 0 R] /Limits [(page.43) (page.48)] >> endobj -2965 0 obj << +2966 0 obj << /Names [(page.49) 1084 0 R (page.5) 470 0 R (page.50) 1115 0 R (page.51) 1125 0 R (page.52) 1150 0 R (page.53) 1169 0 R] /Limits [(page.49) (page.53)] >> endobj -2966 0 obj << +2967 0 obj << /Names [(page.54) 1195 0 R (page.55) 1206 0 R (page.56) 1212 0 R (page.57) 1216 0 R (page.58) 1220 0 R (page.59) 1226 0 R] /Limits [(page.54) (page.59)] >> endobj -2967 0 obj << +2968 0 obj << /Names [(page.6) 508 0 R (page.60) 1236 0 R (page.61) 1253 0 R (page.62) 1271 0 R (page.63) 1279 0 R (page.64) 1293 0 R] /Limits [(page.6) (page.64)] >> endobj -2968 0 obj << +2969 0 obj << /Names [(page.65) 1355 0 R (page.66) 1362 0 R (page.67) 1383 0 R (page.68) 1402 0 R (page.69) 1415 0 R (page.7) 539 0 R] /Limits [(page.65) (page.7)] >> endobj -2969 0 obj << +2970 0 obj << /Names [(page.70) 1422 0 R (page.71) 1439 0 R (page.72) 1457 0 R (page.73) 1468 0 R (page.74) 1489 0 R (page.75) 1502 0 R] /Limits [(page.70) (page.75)] >> endobj -2970 0 obj << +2971 0 obj << /Names [(page.76) 1516 0 R (page.77) 1528 0 R (page.78) 1545 0 R (page.79) 1550 0 R (page.8) 544 0 R (page.80) 1555 0 R] /Limits [(page.76) (page.80)] >> endobj -2971 0 obj << +2972 0 obj << /Names [(page.81) 1563 0 R (page.82) 1572 0 R (page.83) 1584 0 R (page.84) 1598 0 R (page.85) 1609 0 R (page.86) 1644 0 R] /Limits [(page.81) (page.86)] >> endobj -2972 0 obj << +2973 0 obj << /Names [(page.87) 1672 0 R (page.88) 1694 0 R (page.89) 1719 0 R (page.9) 548 0 R (page.90) 1741 0 R (page.91) 1765 0 R] /Limits [(page.87) (page.91)] >> endobj -2973 0 obj << +2974 0 obj << /Names [(page.92) 1799 0 R (page.93) 1830 0 R (page.94) 1838 0 R (page.95) 1858 0 R (page.96) 1872 0 R (page.97) 1913 0 R] /Limits [(page.92) (page.97)] >> endobj -2974 0 obj << +2975 0 obj << /Names [(page.98) 1933 0 R (page.99) 1964 0 R (part*.2) 642 0 R (part*.4) 1227 0 R (section*.3) 643 0 R (section.2.1) 10 0 R] /Limits [(page.98) (section.2.1)] >> endobj -2975 0 obj << +2976 0 obj << /Names [(section.2.2) 14 0 R (section.3.1) 34 0 R (section.3.2) 38 0 R (section.4.1) 62 0 R (section.4.2) 90 0 R (section.5.1) 114 0 R] /Limits [(section.2.2) (section.5.1)] >> endobj -2976 0 obj << +2977 0 obj << /Names [(section.5.2) 138 0 R (section.5.3) 162 0 R (section.6.1) 170 0 R (section.6.2) 174 0 R (section.6.3) 194 0 R (section.7.1) 226 0 R] /Limits [(section.5.2) (section.7.1)] >> endobj -2977 0 obj << +2978 0 obj << /Names [(section.7.2) 230 0 R (section.7.3) 246 0 R (section.7.4) 266 0 R (section.7.5) 270 0 R (section.7.6) 282 0 R (section.8.1) 290 0 R] /Limits [(section.7.2) (section.8.1)] >> endobj -2978 0 obj << +2979 0 obj << /Names [(section.8.2) 294 0 R (section.8.3) 298 0 R (section.8.4) 310 0 R (section.8.5) 314 0 R (section.9.1) 322 0 R (section.9.2) 326 0 R] /Limits [(section.8.2) (section.9.2)] >> endobj -2979 0 obj << +2980 0 obj << /Names [(section.9.3) 330 0 R (section.9.4) 334 0 R (section.9.5) 338 0 R (subfigure.2.2.1) 592 0 R (subfigure.2.2.2) 593 0 R (subfigure.2.3.1) 602 0 R] /Limits [(section.9.3) (subfigure.2.3.1)] >> endobj -2980 0 obj << +2981 0 obj << /Names [(subfigure.2.3.2) 603 0 R (subfigure.4.1.1) 706 0 R (subfigure.4.1.2) 707 0 R (subfigure.4.1.3) 708 0 R (subfigure.4.2.1) 716 0 R (subfigure.4.2.2) 717 0 R] /Limits [(subfigure.2.3.2) (subfigure.4.2.2)] >> endobj -2981 0 obj << +2982 0 obj << /Names [(subfigure.4.2.3) 718 0 R (subfigure.4.3.1) 719 0 R (subfigure.4.3.2) 720 0 R (subfigure.4.3.3) 721 0 R (subfigure.4.4.1) 768 0 R (subfigure.4.4.2) 769 0 R] /Limits [(subfigure.4.2.3) (subfigure.4.4.2)] >> endobj -2982 0 obj << +2983 0 obj << /Names [(subfigure.4.4.3) 770 0 R (subfigure.4.4.4) 771 0 R (subfigure.4.4.5) 756 0 R (subfigure.4.4.6) 772 0 R (subfigure.4.4.7) 773 0 R (subfigure.4.4.8) 774 0 R] /Limits [(subfigure.4.4.3) (subfigure.4.4.8)] >> endobj -2983 0 obj << +2984 0 obj << /Names [(subfigure.4.4.9) 775 0 R (subfigure.4.5.1) 794 0 R (subfigure.4.5.2) 795 0 R (subfigure.4.5.3) 796 0 R (subfigure.4.6.1) 809 0 R (subfigure.4.6.2) 810 0 R] /Limits [(subfigure.4.4.9) (subfigure.4.6.2)] >> endobj -2984 0 obj << +2985 0 obj << /Names [(subfigure.4.6.3) 811 0 R (subfigure.4.6.4) 812 0 R (subfigure.4.8.1) 829 0 R (subfigure.4.8.2) 830 0 R (subfigure.5.1.1) 900 0 R (subfigure.5.1.2) 901 0 R] /Limits [(subfigure.4.6.3) (subfigure.5.1.2)] >> endobj -2985 0 obj << +2986 0 obj << /Names [(subfigure.5.1.3) 902 0 R (subfigure.5.1.4) 903 0 R (subfigure.5.1.5) 909 0 R (subfigure.5.1.6) 904 0 R (subfigure.5.10.1) 1171 0 R (subfigure.5.10.2) 1172 0 R] /Limits [(subfigure.5.1.3) (subfigure.5.10.2)] >> endobj -2986 0 obj << +2987 0 obj << /Names [(subfigure.5.2.1) 967 0 R (subfigure.5.2.2) 968 0 R (subfigure.5.2.3) 969 0 R (subfigure.5.2.4) 970 0 R (subfigure.5.3.1) 990 0 R (subfigure.5.3.2) 991 0 R] /Limits [(subfigure.5.2.1) (subfigure.5.3.2)] >> endobj -2987 0 obj << +2988 0 obj << /Names [(subfigure.5.3.3) 992 0 R (subfigure.5.3.4) 993 0 R (subfigure.5.4.1) 994 0 R (subfigure.5.4.2) 995 0 R (subfigure.5.4.3) 996 0 R (subfigure.5.4.4) 997 0 R] /Limits [(subfigure.5.3.3) (subfigure.5.4.4)] >> endobj -2988 0 obj << +2989 0 obj << /Names [(subfigure.5.5.1) 1030 0 R (subfigure.5.5.2) 1031 0 R (subfigure.5.5.3) 1032 0 R (subfigure.5.5.4) 1033 0 R (subfigure.5.7.1) 1126 0 R (subfigure.5.7.2) 1127 0 R] /Limits [(subfigure.5.5.1) (subfigure.5.7.2)] >> endobj -2989 0 obj << +2990 0 obj << /Names [(subfigure.5.7.3) 1128 0 R (subfigure.5.7.4) 1129 0 R (subfigure.5.8.1) 1130 0 R (subfigure.5.8.2) 1131 0 R (subfigure.5.8.3) 1132 0 R (subfigure.5.9.1) 1157 0 R] /Limits [(subfigure.5.7.3) (subfigure.5.9.1)] >> endobj -2990 0 obj << +2991 0 obj << /Names [(subfigure.5.9.2) 1158 0 R (subfigure.6.10.1) 1503 0 R (subfigure.6.10.2) 1504 0 R (subfigure.6.10.3) 1505 0 R (subfigure.6.12.1) 1529 0 R (subfigure.6.12.2) 1530 0 R] /Limits [(subfigure.5.9.2) (subfigure.6.12.2)] >> endobj -2991 0 obj << +2992 0 obj << /Names [(subfigure.6.2.1) 1363 0 R (subfigure.6.2.2) 1364 0 R (subfigure.6.2.3) 1365 0 R (subfigure.6.2.4) 1366 0 R (subfigure.6.2.5) 1367 0 R (subfigure.6.2.6) 1368 0 R] /Limits [(subfigure.6.2.1) (subfigure.6.2.6)] >> endobj -2992 0 obj << +2993 0 obj << /Names [(subfigure.6.2.7) 1369 0 R (subfigure.6.2.8) 1370 0 R (subfigure.6.3.1) 1371 0 R (subfigure.6.3.2) 1372 0 R (subfigure.6.3.3) 1373 0 R (subfigure.6.4.1) 1358 0 R] /Limits [(subfigure.6.2.7) (subfigure.6.4.1)] >> endobj -2993 0 obj << +2994 0 obj << /Names [(subfigure.6.4.2) 1384 0 R (subfigure.6.7.1) 1423 0 R (subfigure.6.7.2) 1424 0 R (subfigure.6.7.3) 1425 0 R (subfigure.6.9.1) 1461 0 R (subfigure.6.9.2) 1462 0 R] /Limits [(subfigure.6.4.2) (subfigure.6.9.2)] >> endobj -2994 0 obj << +2995 0 obj << /Names [(subfigure.6.9.3) 1463 0 R (subfigure.6.9.4) 1469 0 R (subfigure.6.9.5) 1464 0 R (subfigure.6.9.6) 1470 0 R (subfigure.7.1.1) 1573 0 R (subfigure.7.1.2) 1574 0 R] /Limits [(subfigure.6.9.3) (subfigure.7.1.2)] >> endobj -2995 0 obj << +2996 0 obj << /Names [(subfigure.7.10.1) 1754 0 R (subfigure.7.10.2) 1755 0 R (subfigure.7.10.3) 1756 0 R (subfigure.7.10.4) 1788 0 R (subfigure.7.10.5) 1789 0 R (subfigure.7.12.1) 1839 0 R] /Limits [(subfigure.7.10.1) (subfigure.7.12.1)] >> endobj -2996 0 obj << +2997 0 obj << /Names [(subfigure.7.12.2) 1840 0 R (subfigure.7.13.1) 1859 0 R (subfigure.7.13.2) 1860 0 R (subfigure.7.13.3) 1861 0 R (subfigure.7.13.4) 1862 0 R (subfigure.7.13.5) 1863 0 R] /Limits [(subfigure.7.12.2) (subfigure.7.13.5)] >> endobj -2997 0 obj << +2998 0 obj << /Names [(subfigure.7.13.6) 1864 0 R (subfigure.7.13.7) 1865 0 R (subfigure.7.13.8) 1866 0 R (subfigure.7.14.1) 1934 0 R (subfigure.7.14.10) 1943 0 R (subfigure.7.14.11) 1944 0 R] /Limits [(subfigure.7.13.6) (subfigure.7.14.11)] >> endobj -2998 0 obj << +2999 0 obj << /Names [(subfigure.7.14.12) 1945 0 R (subfigure.7.14.13) 1946 0 R (subfigure.7.14.14) 1947 0 R (subfigure.7.14.15) 1948 0 R (subfigure.7.14.16) 1949 0 R (subfigure.7.14.17) 1950 0 R] /Limits [(subfigure.7.14.12) (subfigure.7.14.17)] >> endobj -2999 0 obj << +3000 0 obj << /Names [(subfigure.7.14.18) 1951 0 R (subfigure.7.14.19) 1952 0 R (subfigure.7.14.2) 1935 0 R (subfigure.7.14.20) 1953 0 R (subfigure.7.14.21) 1954 0 R (subfigure.7.14.22) 1955 0 R] /Limits [(subfigure.7.14.18) (subfigure.7.14.22)] >> endobj -3000 0 obj << +3001 0 obj << /Names [(subfigure.7.14.23) 1956 0 R (subfigure.7.14.24) 1957 0 R (subfigure.7.14.25) 1920 0 R (subfigure.7.14.3) 1936 0 R (subfigure.7.14.4) 1937 0 R (subfigure.7.14.5) 1938 0 R] /Limits [(subfigure.7.14.23) (subfigure.7.14.5)] >> endobj -3001 0 obj << +3002 0 obj << /Names [(subfigure.7.14.6) 1939 0 R (subfigure.7.14.7) 1940 0 R (subfigure.7.14.8) 1941 0 R (subfigure.7.14.9) 1942 0 R (subfigure.7.15.1) 1965 0 R (subfigure.7.15.2) 1966 0 R] /Limits [(subfigure.7.14.6) (subfigure.7.15.2)] >> endobj -3002 0 obj << +3003 0 obj << /Names [(subfigure.7.15.3) 1967 0 R (subfigure.7.15.4) 1968 0 R (subfigure.7.15.5) 1969 0 R (subfigure.7.15.6) 1970 0 R (subfigure.7.4.1) 1645 0 R (subfigure.7.4.2) 1646 0 R] /Limits [(subfigure.7.15.3) (subfigure.7.4.2)] >> endobj -3003 0 obj << +3004 0 obj << /Names [(subfigure.7.4.3) 1647 0 R (subfigure.7.4.4) 1648 0 R (subfigure.7.4.5) 1649 0 R (subfigure.7.4.6) 1650 0 R (subfigure.7.5.1) 1673 0 R (subfigure.7.5.10) 1682 0 R] /Limits [(subfigure.7.4.3) (subfigure.7.5.10)] >> endobj -3004 0 obj << +3005 0 obj << /Names [(subfigure.7.5.11) 1683 0 R (subfigure.7.5.12) 1684 0 R (subfigure.7.5.13) 1653 0 R (subfigure.7.5.2) 1674 0 R (subfigure.7.5.3) 1675 0 R (subfigure.7.5.4) 1676 0 R] /Limits [(subfigure.7.5.11) (subfigure.7.5.4)] >> endobj -3005 0 obj << +3006 0 obj << /Names [(subfigure.7.5.5) 1677 0 R (subfigure.7.5.6) 1678 0 R (subfigure.7.5.7) 1679 0 R (subfigure.7.5.8) 1680 0 R (subfigure.7.5.9) 1681 0 R (subfigure.7.7.1) 1695 0 R] /Limits [(subfigure.7.5.5) (subfigure.7.7.1)] >> endobj -3006 0 obj << +3007 0 obj << /Names [(subfigure.7.7.10) 1704 0 R (subfigure.7.7.11) 1705 0 R (subfigure.7.7.12) 1706 0 R (subfigure.7.7.2) 1696 0 R (subfigure.7.7.3) 1697 0 R (subfigure.7.7.4) 1698 0 R] /Limits [(subfigure.7.7.10) (subfigure.7.7.4)] >> endobj -3007 0 obj << +3008 0 obj << /Names [(subfigure.7.7.5) 1699 0 R (subfigure.7.7.6) 1700 0 R (subfigure.7.7.7) 1701 0 R (subfigure.7.7.8) 1702 0 R (subfigure.7.7.9) 1703 0 R (subfigure.7.8.1) 1721 0 R] /Limits [(subfigure.7.7.5) (subfigure.7.8.1)] >> endobj -3008 0 obj << -/Names [(subfigure.7.8.2) 1722 0 R (subfigure.7.8.3) 1723 0 R (subfigure.7.8.4) 1724 0 R (subfigure.7.9.1) 1750 0 R (subfigure.7.9.2) 1751 0 R (subfigure.8.4.1) 2160 0 R] +3009 0 obj << +/Names [(subfigure.7.8.2) 1722 0 R (subfigure.7.8.3) 1723 0 R (subfigure.7.8.4) 1724 0 R (subfigure.7.9.1) 1750 0 R (subfigure.7.9.2) 1751 0 R (subfigure.8.4.1) 2161 0 R] /Limits [(subfigure.7.8.2) (subfigure.8.4.1)] >> endobj -3009 0 obj << -/Names [(subfigure.8.4.2) 2161 0 R (subfigure.9.1.1) 2252 0 R (subfigure.9.1.2) 2253 0 R (subfigure.9.2.1) 2574 0 R (subfigure.9.2.2) 2575 0 R (subsection.2.2.1) 18 0 R] +3010 0 obj << +/Names [(subfigure.8.4.2) 2162 0 R (subfigure.9.1.1) 2253 0 R (subfigure.9.1.2) 2254 0 R (subfigure.9.2.1) 2575 0 R (subfigure.9.2.2) 2576 0 R (subsection.2.2.1) 18 0 R] /Limits [(subfigure.8.4.2) (subsection.2.2.1)] >> endobj -3010 0 obj << +3011 0 obj << /Names [(subsection.2.2.2) 22 0 R (subsection.2.2.3) 26 0 R (subsection.3.2.1) 42 0 R (subsection.3.2.2) 46 0 R (subsection.3.2.3) 50 0 R (subsection.3.2.4) 54 0 R] /Limits [(subsection.2.2.2) (subsection.3.2.4)] >> endobj -3011 0 obj << +3012 0 obj << /Names [(subsection.4.1.1) 66 0 R (subsection.4.1.2) 86 0 R (subsection.4.2.1) 94 0 R (subsection.4.2.2) 98 0 R (subsection.4.2.3) 102 0 R (subsection.4.2.4) 106 0 R] /Limits [(subsection.4.1.1) (subsection.4.2.4)] >> endobj -3012 0 obj << +3013 0 obj << /Names [(subsection.5.1.1) 118 0 R (subsection.5.1.2) 122 0 R (subsection.5.1.3) 126 0 R (subsection.5.1.4) 130 0 R (subsection.5.1.5) 134 0 R (subsection.5.2.1) 142 0 R] /Limits [(subsection.5.1.1) (subsection.5.2.1)] >> endobj -3013 0 obj << +3014 0 obj << /Names [(subsection.5.2.2) 146 0 R (subsection.5.2.3) 150 0 R (subsection.5.2.4) 154 0 R (subsection.5.2.5) 158 0 R (subsection.6.2.1) 178 0 R (subsection.6.2.2) 182 0 R] /Limits [(subsection.5.2.2) (subsection.6.2.2)] >> endobj -3014 0 obj << +3015 0 obj << /Names [(subsection.6.2.3) 186 0 R (subsection.6.2.4) 190 0 R (subsection.6.3.1) 198 0 R (subsection.6.3.2) 202 0 R (subsection.6.3.3) 210 0 R (subsection.6.3.4) 214 0 R] /Limits [(subsection.6.2.3) (subsection.6.3.4)] >> endobj -3015 0 obj << +3016 0 obj << /Names [(subsection.6.3.5) 218 0 R (subsection.7.2.1) 234 0 R (subsection.7.3.1) 250 0 R (subsection.7.3.2) 254 0 R (subsection.7.3.3) 258 0 R (subsection.7.5.1) 274 0 R] /Limits [(subsection.6.3.5) (subsection.7.5.1)] >> endobj -3016 0 obj << +3017 0 obj << /Names [(subsection.7.5.2) 278 0 R (subsection.8.3.1) 302 0 R (subsection.8.3.2) 306 0 R (subsubsection.4.1.1.1) 70 0 R (subsubsection.4.1.1.2) 74 0 R (subsubsection.4.1.1.3) 78 0 R] /Limits [(subsection.7.5.2) (subsubsection.4.1.1.3)] >> endobj -3017 0 obj << +3018 0 obj << /Names [(subsubsection.4.1.1.4) 82 0 R (subsubsection.6.3.2.1) 206 0 R (subsubsection.7.2.1.1) 238 0 R (subsubsection.7.2.1.2) 242 0 R 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/Size 3065 -/Root 3063 0 R -/Info 3064 0 R -/ID [ ] >> +<< /Size 3066 +/Root 3064 0 R +/Info 3065 0 R +/ID [<12F6DB4B3ADC1BC28BBC8135E9718F57> <12F6DB4B3ADC1BC28BBC8135E9718F57>] >> startxref -37692813 +37693019 %%EOF diff --git a/THESE/these.tex b/THESE/these.tex index 00bad4e..600f52a 100644 --- a/THESE/these.tex +++ b/THESE/these.tex @@ -74,8 +74,8 @@ %% Set the English abstract \thesisabstract[english]{ %Recent graphical processing units (GPU), bring parallel computing capabilities to almost every developper, -In theory, modern graphical processing units (GPUs) make parallel programming accessible to all, and have triggered widespread interest among researchers or developers of all disciplines, with the hope of dramatically increasing processing speeds. Nevertheless, obtaining such processing speeds as expected cannot be done without considerable designing efforts : as an answer, we propose in this thesis, two GPU-based methods leading to fast implementations of several algorithms targeted to processing noisy images. One of them consists in porting the segmentation algorithm named \textit{snake}, with the effect of extending its processing capacity and performance. A second involves an innovative GPU-specific algorithm, based on searching for level lines within gray-level or color images to reduce gaussian noise, whose signal-to-noise ratio is particularly interesting. -Through extremely fine-tuned management of the different memory types available on GPUs, we have also conferred unprecedent flow rates to the median filter, making it able to process over 5 million pixels per second. Eventually, we extended the above methods to the more generic convolution filter, and showed they out-perform the fastest implementations known to date, with over 7 million pixels per second. In addition, we provide an on-line application that enables any developer to automatically generate operational source code of our filters. +In theory, modern graphical processing units (GPUs) make parallel programming accessible to all, and have triggered widespread interest among researchers or developers of all disciplines, with the hope of dramatically increasing processing speeds. Nevertheless, obtaining such performances cannot be done without considerable designing efforts : as an answer, we propose two GPU-based methods leading to fast implementations of several algorithms targeted to processing noisy images. One of them consists in porting the segmentation algorithm named \textit{snake}, with the effect of extending its processing capacity and performance. A second involves a innovative GPU-specific algorithm, based on searching for level lines within gray-level or color images to reduce gaussian noise, whose quality-to-speed ratio is particularly interesting. +Through extremely fine-tuned management of the different memory types available on GPUs, we have also conferred unprecedent flow rates to the median filter, making it able to process over 5 billion pixels per second. Eventually, we extended the above methods to the more generic convolution filter, and showed they out-perform the fastest implementations known to date, with over 7 billion pixels per second. In addition, we provide an on-line application that enables any developer to automatically generate operational source code of our filters. } %%-------------------- %% Set the English keywords. They only appear if @@ -85,8 +85,8 @@ Through extremely fine-tuned management of the different memory types available %% -------------------- %% Set the French abstract \thesisabstract[french]{ - Les cartes graphiques modernes (GPU) mettent, en théorie, la programmation parallèle à la portée de tous. Ces facilités ont éveillé l'intérêt des chercheurs et développeurs de toutes disciplines, qui ont tenté de tirer parti des performances élevées de ces matériels. Cependant; d'importants efforts de conception sont souvent nécessaires à l'obtention des vitesses de traitement espérées. -Dans cette thèse, nous proposons des méthodes conduisant à des implémentations rapides de plusieurs algorithmes destinés au traitement des images fortement bruitées. La première est une transposition sur GPU d'un algorithme de segmentation dit du \textit{snake} dont la capacité de traitement a été étendue et les performances améliorées. La seconde décrit un algorithme original, basé sur la recherche des lignes de niveaux et conçu spécifiquement pour les GPUs, qui réduit le bruit gaussien dans les images en niveaux de gris ou en couleur et dont le rapport qualité/vitesse est particulièrement intéressant. En concevant une gestion fine des mémoires du GPU, nous avons également conféré un débit de traitement inégalé au filtre médian, pouvant dépasser les 5 milliards de pixels à la seconde. Enfin nous avons étendu l'application de ces techniques à un opérateur beaucoup plus générique, le filtre de convolution, et montré qu'elles permettaient de surpasser les implémentations les plus rapides connues jusqu'alors avec un maximum au delà des 7 milliards de pixels à la seconde. Nous mettons aussi à disposition une application en ligne permettant à tout développeur de générer les codes sources opérationnels des filtres que nous avons décrits. + Les cartes graphiques modernes (GPU) mettent, en théorie, la programmation parallèle à la portée de tous. Ces facilités ont éveillé l'intérêt des chercheurs et développeurs de toutes disciplines, qui ont tenté de tirer parti des performances élevées de ces matériels. Cependant, d'importants efforts de conception sont souvent nécessaires à l'obtention des vitesses de traitement espérées. +Dans cette thèse, nous proposons des méthodes conduisant à des implémentations rapides de plusieurs algorithmes destinés au traitement des images fortement bruitées. La première est une transposition sur GPU d'un algorithme de segmentation dit du \textit{snake} dont la capacité de traitement a été étendue et les performances améliorées. La seconde décrit un algorithme original, basé sur la recherche des lignes de niveaux et conçu spécifiquement pour les GPUs, qui réduit le bruit gaussien dans les images en niveaux de gris ou en couleur et dont le rapport qualité/vitesse est particulièrement intéressant. En concevant une gestion fine des mémoires du GPU, nous avons également conféré un débit de traitement inégalé au filtre médian, pouvant dépasser les 5 milliards de pixels à la seconde. Enfin nous avons étendu l'application de ces techniques à un opérateur beaucoup plus générique, le filtre de convolution, et montré qu'elles permettaient de surpasser les implémentations les plus rapides connues jusqu'alors, avec un maximum au delà des 7 milliards de pixels à la seconde. Nous mettons aussi à disposition une application en ligne permettant à tout développeur de générer les codes sources opérationnels des filtres que nous avons décrits. }