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cibl\u00e9[\/vc_custom_heading][vc_custom_heading heading_semantic=\u00a0\u00bbh3″ text_size=\u00a0\u00bbh1″ uncode_shortcode_id=\u00a0\u00bb193499″]Pr\u00e9sentation g\u00e9n\u00e9rale[\/vc_custom_heading][\/vc_column_inner][\/vc_row_inner][vc_row_inner row_inner_height_percent=\u00a0\u00bb0″ row_padding=\u00a0\u00bb0″ overlay_alpha=\u00a0\u00bb50″ gutter_size=\u00a0\u00bb3″ shift_y=\u00a0\u00bb0″ z_index=\u00a0\u00bb0″ uncode_shortcode_id=\u00a0\u00bb797108″ limit_content=\u00a0\u00bb\u00a0\u00bb][vc_column_inner column_width_percent=\u00a0\u00bb100″ gutter_size=\u00a0\u00bb3″ style=\u00a0\u00bblight\u00a0\u00bb overlay_alpha=\u00a0\u00bb50″ shift_x=\u00a0\u00bb0″ shift_y=\u00a0\u00bb0″ shift_y_down=\u00a0\u00bb0″ z_index=\u00a0\u00bb0″ medium_width=\u00a0\u00bb0″ mobile_width=\u00a0\u00bb0″ width=\u00a0\u00bb1\/1″ uncode_shortcode_id=\u00a0\u00bb154421″][vc_column_text text_lead=\u00a0\u00bbyes\u00a0\u00bb uncode_shortcode_id=\u00a0\u00bb350389″]<\/p>\n

Le projet Exa-DI est charg\u00e9 de mettre en \u0153uvre le processus de co-conception et de co-d\u00e9veloppement au sein de NumPEx afin de d\u00e9velopper une pile logicielle permettant l\u2019utilisation productive des syst\u00e8mes exascale. Pour garantir la productivit\u00e9, la portabilit\u00e9 et la p\u00e9rennit\u00e9 des applications, il est n\u00e9cessaire d\u2019am\u00e9liorer les m\u00e9thodes de d\u00e9veloppement de logiciels scientifiques en s\u2019appuyant sur des composants de haute qualit\u00e9 et r\u00e9guli\u00e8rement mis \u00e0 jour (biblioth\u00e8ques, frameworks et outils). Cet objectif est soutenu par une \u00e9quipe nationale de d\u00e9veloppement et d\u2019ing\u00e9nierie logicielle (la Computational Data Team ; CDT) compos\u00e9e de chercheurs et d\u2019ing\u00e9nieurs poss\u00e9dant des expertises vari\u00e9es. La CDT comprend une \u00e9quipe de d\u00e9veloppement (CDT-DT) charg\u00e9e de co-d\u00e9velopper des proxy\/mini-applications bas\u00e9es sur des motifs algorithmiques communs \u00e0 plusieurs d\u00e9monstrateurs d\u2019applications (AD), ainsi qu\u2019une \u00e9quipe de soutien (CDT-ET) charg\u00e9e du packaging logiciel (par exemple, Guix, Spack) et du d\u00e9ploiement. Les activit\u00e9s de la CDT sont men\u00e9es selon une m\u00e9thodologie agile reposant sur un processus it\u00e9ratif ; cette approche est guid\u00e9e par les besoins des applications et s\u2019articule autour de motifs algorithmiques partag\u00e9s (calcul, communication) ainsi que de m\u00e9thodologies et de technologies communes de packaging et de d\u00e9ploiement. <\/p>\n

\u00c0 la suite d’ateliers de co-conception et de co-d\u00e9veloppement ax\u00e9s sur les motifs algorithmiques identifi\u00e9s lors de l’analyse conjointe des d\u00e9monstrateurs d’application (AD), six groupes de travail (GT), anim\u00e9s par des membres des \u00e9quipes AD, sont d\u00e9sormais op\u00e9rationnels. Leurs activit\u00e9s de co-conception et d\u2019ing\u00e9nierie CDT sont h\u00e9berg\u00e9es dans un espace GitLab nomm\u00e9 NumPEx-PC5<\/a>. De plus, une infrastructure web<\/a> a \u00e9t\u00e9 mise en place pour prendre en charge l\u2019int\u00e9gration logicielle et les plateformes de collaboration, notamment en regroupant l\u2019ensemble des modules de formation. <\/p>\n

[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_single_image media=\u00a0\u00bb331″ media_width_percent=\u00a0\u00bb50″ alignment=\u00a0\u00bbcenter\u00a0\u00bb uncode_shortcode_id=\u00a0\u00bb879828″][vc_row_inner limit_content=\u00a0\u00bb\u00a0\u00bb][vc_column_inner width=\u00a0\u00bb1\/1″][vc_empty_space empty_h=\u00a0\u00bb2″][vc_separator sep_color=\u00a0\u00bbcolor-nhtu\u00a0\u00bb uncode_shortcode_id=\u00a0\u00bb794634″ sep_color_type=\u00a0\u00bbuncode-palette\u00a0\u00bb][vc_empty_space empty_h=\u00a0\u00bb2″ el_id=\u00a0\u00bbnav-consortium-&-workpackages\u00a0\u00bb][\/vc_column_inner][\/vc_row_inner][vc_row_inner row_inner_height_percent=\u00a0\u00bb0″ row_padding=\u00a0\u00bb0″ overlay_alpha=\u00a0\u00bb50″ gutter_size=\u00a0\u00bb3″ shift_y=\u00a0\u00bb0″ z_index=\u00a0\u00bb0″ uncode_shortcode_id=\u00a0\u00bb797108″ limit_content=\u00a0\u00bb\u00a0\u00bb][vc_column_inner column_width_percent=\u00a0\u00bb100″ align_horizontal=\u00a0\u00bbalign_center\u00a0\u00bb gutter_size=\u00a0\u00bb3″ style=\u00a0\u00bblight\u00a0\u00bb overlay_alpha=\u00a0\u00bb50″ shift_x=\u00a0\u00bb0″ shift_y=\u00a0\u00bb0″ shift_y_down=\u00a0\u00bb0″ z_index=\u00a0\u00bb0″ medium_width=\u00a0\u00bb0″ mobile_width=\u00a0\u00bb0″ width=\u00a0\u00bb1\/1″ uncode_shortcode_id=\u00a0\u00bb205708″][vc_custom_heading text_color=\u00a0\u00bbaccent\u00a0\u00bb heading_semantic=\u00a0\u00bbh6″ text_size=\u00a0\u00bbh6″ text_transform=\u00a0\u00bbuppercase\u00a0\u00bb uncode_shortcode_id=\u00a0\u00bb493608″ text_color_type=\u00a0\u00bbuncode-palette\u00a0\u00bb]Partenaires et organisation[\/vc_custom_heading][vc_custom_heading heading_semantic=\u00a0\u00bbh3″ text_size=\u00a0\u00bbh1″ uncode_shortcode_id=\u00a0\u00bb372426″]Consortium et lots de travail[\/vc_custom_heading][vc_column_text uncode_shortcode_id=\u00a0\u00bb168463″]Le projet Exa-DI a pour objectif d\u2019acc\u00e9l\u00e9rer le d\u00e9veloppement d\u2019applications exascale, en proposant des \u00ab kits de d\u00e9veloppement logiciel \u00bb (SDK) \u2013 bas\u00e9s sur les m\u00e9thodes, les frameworks, les biblioth\u00e8ques et les composants logiciels d\u00e9velopp\u00e9s dans le cadre des autres projets NumPEx Exa-MA, Exa-SofT, Exa-DoST et Exa-AToW \u2014 qui offrent des solutions durables, performantes et portables aux principaux motifs algorithmiques de calcul et de communication rencontr\u00e9s dans les d\u00e9monstrateurs d\u2019applications exascale. Ces motifs sont trait\u00e9s \u00e0 l\u2019aide d\u2019applications proxy dans le cadre d\u2019un processus it\u00e9ratif de co-conception et de co-d\u00e9veloppement men\u00e9 en collaboration avec les \u00e9quipes scientifiques et industrielles charg\u00e9es des applications.[\/vc_column_text][vc_empty_space 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Activit\u00e9s d’Exa-DI :<\/h3>\n
    \n
  • Analyse conjointe des d\u00e9monstrateurs d’application (AD) : 27 d\u00e9monstrateurs d’application couvrant un large \u00e9ventail de domaines des sciences et de l’ing\u00e9nierie computationnelles, avec un nombre croissant d’applications de flux de travail computationnels hybrides alliant IA et HPC.<\/span><\/li>\n
  • Ateliers Exa-DI en 2023 : discr\u00e9tisation efficace des \u00e9quations aux d\u00e9riv\u00e9es partielles \u00e0 l’exascale et, en 2024, affinement adaptatif des maillages \u00e0 structure par blocs \u00e0 l’exascale, intelligence artificielle pour le calcul haute performance \u00e0 l’exascale et flux de traitement \u00e0 grande \u00e9chelle pour le SKA<\/span><\/li>\n<\/ul>\n

    Groupes de travail sur la conception et le d\u00e9veloppement participatifs :<\/h4>\n
    \n

    Le groupe de travail WG1 se concentre sur des motifs et sous-motifs algorithmiques transversaux sp\u00e9cifiques afin d’am\u00e9liorer le d\u00e9veloppement de logiciels de nouvelle g\u00e9n\u00e9ration bas\u00e9s sur des \u00e9l\u00e9ments finis\/spectraux d’ordre \u00e9lev\u00e9, qui permettent \u00e0 toute une gamme d’applications de fonctionner efficacement sur du mat\u00e9riel exascale gr\u00e2ce \u00e0 des applications proxy et des mini-applications.<\/p>\n

    L’objectif du groupe de travail 2 (WG2) est de d\u00e9velopper une application de d\u00e9monstration illustrant la g\u00e9n\u00e9ration et le raffinement de maillages non structur\u00e9s comportant des dizaines de milliards d’\u00e9l\u00e9ments, et proposant un ensemble de services permettant de discr\u00e9tiser les op\u00e9rateurs diff\u00e9rentiels partiels bas\u00e9s sur la physique ; ce groupe de travail est actuellement en train de cr\u00e9er, de rassembler et d’int\u00e9grer les \u00e9l\u00e9ments de base n\u00e9cessaires \u00e0 cette application de d\u00e9monstration.<\/p>\n

    Au sein du WG3, un test de performance \u00ab normalis\u00e9 \u00bb visant \u00e0 \u00e9valuer et \u00e0 comparer les performances de diff\u00e9rents cadres AMR sur diverses applications physiques est en cours de mise en \u0153uvre ; le d\u00e9fi consiste \u00e0 trouver des solutions communes pour rem\u00e9dier au goulot d’\u00e9tranglement des codes AMR.<\/p>\n

    Pour le groupe de travail 4 (WG4), le d\u00e9fi consiste \u00e0 d\u00e9terminer comment faire \u00e9voluer \u00e0 grande \u00e9chelle les flux de travail hybrides IA-HPC distribu\u00e9s, tant pour le calcul des inverses que pour l’imagerie, sur de grandes infrastructures HPC, puis \u00e0 identifier et \u00e9valuer les composants fondamentaux d’apprentissage automatique \u00e0 utiliser, ainsi qu’\u00e0 d\u00e9velopper les modules logiciels n\u00e9cessaires pour exploiter pleinement et faire \u00e9voluer ces cas d’utilisation.<\/p>\n

    Lancement : Groupe de travail n\u00b0<\/strong> 5 (WG5) \u2013 Flux de travail HPC coupl\u00e9s \u00e0 l’IA \u2013 Mod\u00e8les de substitution \u00e0 l’\u00e9chelle exascale et Groupe de travail n\u00b0 6 (WG6) \u2013 Flux de travail de traitement \u00e0 grande \u00e9chelle bas\u00e9s sur l’IA \u00e0 l’\u00e9chelle exascale<\/p>\n<\/div>\n

    Package et d\u00e9ploiement de logiciels @exascale<\/h4>\n
      \n
    • Renforcer et soutenir l’adoption des technologies logicielles de m\u00e9ta-compilation : Spack, Guix-HPC<\/span><\/li>\n
    • Mettre en place des collaborations avec les centres nationaux et r\u00e9gionaux de calcul haute performance (HPC) afin de soutenir les projets Spack et Guix-HPC<\/span><\/li>\n
    • \u00c9laborer des supports de formation, organiser des webinaires et des hackathons consacr\u00e9s \u00e0 Spack, Guix-HPC et au catalogue de logiciels NumPeX<\/span><\/li>\n
    • \u00c9laborer et encourager l’adoption des directives relatives au logiciel NumPeX<\/span><\/li>\n
    • Promouvoir les m\u00e9thodologies d’\u00e9valuation de l’int\u00e9gration des comp\u00e9tences et de la performance<\/span><\/li>\n<\/ul>\n

      [\/vc_column_text][vc_empty_space empty_h=\u00a0\u00bb2″][vc_raw_html]<!-- ============================================================
  NumPEx — Page Exa-DI — Exa-DI software production (7 cartes à lien direct)
  Naos Agency · 2026-08-14 · Bloc autonome pour Uncode (WP Bakery)
  Row ID : nav-results-exa-di-en — placé SOUS les textes d'activités
  (Exa-DI activities / WGs / packaging, natifs Uncode), AVANT
  exa-di-highlights.html.
  Gabarit « Marine d'État » Exa-SofT : carte entière = lien externe
  (target="_blank" rel="noopener"), flèche ↗. Descriptifs verbatim
  du docx Results (PC5, 29/07). Carte NumPEx Software Catalog :
  lien principal = site, 2e lien = dépôt GitHub (pastille).
  2e lien BenchOpt dispo si souhaité : https://github.com/benchopt/benchopt
  Textes en dur : WPML. Typo héritée du thème (Poppins en secours).
============================================================ -->
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<div style="max-width:1140px;margin:0 auto;font-family:'Poppins',system-ui,-apple-system,'Segoe UI',sans-serif">
<h3 style="margin:0;text-align:center;font-size:20px;font-weight:700;color:#1C1E24">Développement du logiciel Exa-DI</h3>
<div class="npxdi-sw" style="margin-top:26px">
  <a href="https://deepinv.github.io/deepinv/" target="_blank" rel="noopener" style="position:relative;overflow:hidden;background:#16227A;border:1px solid rgba(255,255,255,.14);border-radius:12px;padding:20px;display:flex;flex-direction:column;gap:7px;box-shadow:0 2px 10px rgba(22,34,122,.18);text-decoration:none">
    <span aria-hidden="true" style="position:absolute;bottom:-42px;right:-42px;width:96px;height:96px;border:2px solid rgba(240,129,60,.55);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;bottom:-58px;right:-28px;width:96px;height:96px;border:2px solid rgba(127,163,216,.4);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;top:14px;right:16px;font-size:12px;color:#F0813C">↗</span>
    <span style="font-size:16.5px;font-weight:700;color:#FFFFFF;letter-spacing:.02em;padding-right:22px">Extension de DeepInverse et BenchOpt</span>
    <span style="font-size:13.5px;color:#C4D0EC;line-height:1.5">Extension des frameworks DeepInverse et BenchOpt basés sur PyTorch, dotés d’une fonctionnalité de distribution permettant de prendre en charge le traitement parallèle et les tests de performance sur plusieurs GPU.</span>
  </a>
  <a href="https://gitlab.inria.fr/numpex-pc5/wp2-co-design/proxy-FUn" target="_blank" rel="noopener" style="position:relative;overflow:hidden;background:#16227A;border:1px solid rgba(255,255,255,.14);border-radius:12px;padding:20px;display:flex;flex-direction:column;gap:7px;box-shadow:0 2px 10px rgba(22,34,122,.18);text-decoration:none">
    <span aria-hidden="true" style="position:absolute;bottom:-42px;right:-42px;width:96px;height:96px;border:2px solid rgba(240,129,60,.55);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;bottom:-58px;right:-28px;width:96px;height:96px;border:2px solid rgba(127,163,216,.4);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;top:14px;right:16px;font-size:12px;color:#F0813C">↗</span>
    <span style="font-size:16.5px;font-weight:700;color:#FFFFFF;letter-spacing:.02em;padding-right:22px">Proxy-Fun</span>
    <span style="font-size:13.5px;color:#C4D0EC;line-height:1.5">Proxy-Fun est une application proxy destinée aux opérateurs de la méthode des éléments spectraux d'ordre élevé. Elle s'adresse aux solveurs d'ondes acoustiques et élastiques, en fournissant des implémentations de référence permettant d'étudier les paramètres de discrétisation, les formulations des opérateurs et leur impact sur les performances des architectures HPC modernes. </span>
  </a>
  <a href="https://gitlab.inria.fr/numpex-pc5/wp2-co-design/proxy-kernels" target="_blank" rel="noopener" style="position:relative;overflow:hidden;background:#16227A;border:1px solid rgba(255,255,255,.14);border-radius:12px;padding:20px;display:flex;flex-direction:column;gap:7px;box-shadow:0 2px 10px rgba(22,34,122,.18);text-decoration:none">
    <span aria-hidden="true" style="position:absolute;bottom:-42px;right:-42px;width:96px;height:96px;border:2px solid rgba(240,129,60,.55);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;bottom:-58px;right:-28px;width:96px;height:96px;border:2px solid rgba(127,163,216,.4);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;top:14px;right:16px;font-size:12px;color:#F0813C">↗</span>
    <span style="font-size:16.5px;font-weight:700;color:#FFFFFF;letter-spacing:.02em;padding-right:22px">Noyaux proxy</span>
    <span style="font-size:13.5px;color:#C4D0EC;line-height:1.5">Proxy-Kernels est une mini-application à vocation de recherche dérivée de Proxy-Fun, axée sur les aspects de discrétisation des méthodes des éléments spectraux (SEM). Elle propose à la fois des formulations classiques des opérateurs SEM basées sur la quadrature et des formulations tensoriales sans matrice, y compris une implémentation basée sur GEMM destinée aux bibliothèques BLAS et aux backends d'accélération. </span>
  </a>
  <a href="https://gitlab.inria.fr/numpex-pc5/wp2-co-design/bendi" target="_blank" rel="noopener" style="position:relative;overflow:hidden;background:#16227A;border:1px solid rgba(255,255,255,.14);border-radius:12px;padding:20px;display:flex;flex-direction:column;gap:7px;box-shadow:0 2px 10px rgba(22,34,122,.18);text-decoration:none">
    <span aria-hidden="true" style="position:absolute;bottom:-42px;right:-42px;width:96px;height:96px;border:2px solid rgba(240,129,60,.55);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;bottom:-58px;right:-28px;width:96px;height:96px;border:2px solid rgba(127,163,216,.4);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;top:14px;right:16px;font-size:12px;color:#F0813C">↗</span>
    <span style="font-size:16.5px;font-weight:700;color:#FFFFFF;letter-spacing:.02em;padding-right:22px">Bendi</span>
    <span style="font-size:13.5px;color:#C4D0EC;line-height:1.5">Bendi est une infrastructure logicielle permettant de réaliser des tests de performance automatisés et reproductibles, tant au niveau des applications qu'au niveau du système. Elle fonctionne actuellement sur SLICES-FR/Grid'5000 et est destinée à Jean Zay, Adastra ou toute autre infrastructure de niveau 1 ou 2. </span>
  </a>
  <a href="https://gitlab.inria.fr/numpex-pc5/wp2-co-design/proxy-geos-hc/-/tree/v0.1.0?ref_type=tags" target="_blank" rel="noopener" style="position:relative;overflow:hidden;background:#16227A;border:1px solid rgba(255,255,255,.14);border-radius:12px;padding:20px;display:flex;flex-direction:column;gap:7px;box-shadow:0 2px 10px rgba(22,34,122,.18);text-decoration:none">
    <span aria-hidden="true" style="position:absolute;bottom:-42px;right:-42px;width:96px;height:96px;border:2px solid rgba(240,129,60,.55);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;bottom:-58px;right:-28px;width:96px;height:96px;border:2px solid rgba(127,163,216,.4);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;top:14px;right:16px;font-size:12px;color:#F0813C">↗</span>
    <span style="font-size:16.5px;font-weight:700;color:#FFFFFF;letter-spacing:.02em;padding-right:22px">Proxy-GEOS</span>
    <span style="font-size:13.5px;color:#C4D0EC;line-height:1.5">Proxy-GEOS est une application proxy qui rassemble un ensemble de codes simples, notamment SEM et FD, permettant de résoudre l’équation des ondes acoustiques du second ordre dans des espaces en 2D et en 3D et représentant des applications réelles, afin de servir d’outil standard pour évaluer et comparer les performances de différents systèmes de calcul haute performance (HPC).</span>
  </a>
  <div style="position:relative;overflow:hidden;background:#16227A;border:1px solid rgba(255,255,255,.14);border-radius:12px;padding:20px;display:flex;flex-direction:column;gap:7px;box-shadow:0 2px 10px rgba(22,34,122,.18)">
    <a href="https://numpex-pc5.gitlabpages.inria.fr/tutorials/projects/index.html" target="_blank" rel="noopener" aria-label="NumPEx Software Catalog — website" style="position:absolute;inset:0;z-index:1"></a>
    <span aria-hidden="true" style="position:absolute;bottom:-42px;right:-42px;width:96px;height:96px;border:2px solid rgba(240,129,60,.55);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;bottom:-58px;right:-28px;width:96px;height:96px;border:2px solid rgba(127,163,216,.4);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;top:14px;right:16px;font-size:12px;color:#F0813C">↗</span>
    <span style="font-size:16.5px;font-weight:700;color:#FFFFFF;letter-spacing:.02em;padding-right:22px">Catalogue des logiciels NumPEx</span>
    <span style="font-size:13.5px;color:#C4D0EC;line-height:1.5">Le catalogue de logiciels NumPEx est une base de données de métadonnées consacrée aux projets logiciels respectant les directives d'intégration logicielle NumPEx. Une présentation conviviale du contenu de cette base de données est disponible sur le site web suivant. </span>
    <a class="npxdi-swrepo" href="https://github.com/numpex/sw-catalog" target="_blank" rel="noopener" style="position:relative;z-index:2;align-self:flex-start;margin-top:auto;text-decoration:none;background:transparent;border:1px solid rgba(255,255,255,.45);color:#FFFFFF;font-size:11.5px;font-weight:600;border-radius:999px;padding:6px 13px;display:inline-flex;align-items:center;gap:6px">Dépôt <span aria-hidden="true">↗</span></a>
  </div>
  <a class="npxdi-sw7" href="https://codeberg.org/guix-science/guix-science" target="_blank" rel="noopener" style="position:relative;overflow:hidden;background:#16227A;border:1px solid rgba(255,255,255,.14);border-radius:12px;padding:20px;display:flex;flex-direction:column;gap:7px;box-shadow:0 2px 10px rgba(22,34,122,.18);text-decoration:none">
    <span aria-hidden="true" style="position:absolute;bottom:-42px;right:-42px;width:96px;height:96px;border:2px solid rgba(240,129,60,.55);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;bottom:-58px;right:-28px;width:96px;height:96px;border:2px solid rgba(127,163,216,.4);border-radius:999px"></span>
    <span aria-hidden="true" style="position:absolute;top:14px;right:16px;font-size:12px;color:#F0813C">↗</span>
    <span style="font-size:16.5px;font-weight:700;color:#FFFFFF;letter-spacing:.02em;padding-right:22px">Unification des dépôts Guix</span>
    <span style="font-size:13.5px;color:#C4D0EC;line-height:1.5">Unification des dépôts de paquets Guix de guix-hpc et guix-science sur la forge Codeberg afin d’améliorer leur visibilité.</span>
  </a>
</div>
</div>
[\/vc_raw_html][vc_empty_space empty_h=\u00a0\u00bb2″][vc_raw_html]<!-- ============================================================
  NumPEx — Page Exa-DI — Highlights (2 cartes → 2 modales)
  Naos Agency · 2026-08-14 · v2 — mécanisme aligné sur
  exa-dost-results-impact.html (correctif Jess 14/08) :
  overlay créé en JS et monté sur document.body (échappe aux
  stacking contexts des rows Uncode), z-index 100000 (au-dessus du
  sous-menu sticky v.6.1), scroll verrouillé, croix cliquable,
  bouton « More details → » identique à Exa-DoST.
  Row ID : nav-results-exa-di-en — APRÈS exa-di-software-production.html,
  avant les listes publications/formations (natives Uncode).
  Contenus verbatim des 2 fiches PC5 en HTML caché traduisible WPML
  (div hidden, cloné à l'ouverture — pattern Exa-DoST).
  ⚠ IMAGES (modale B) : téléverser images-mediatheque/*.png puis
  remplacer les 2 src marqués « SRC-A-REMPLACER ».
  Graphies conservées : « Jean Day » (tracé V4, probable « Jean Zay »).
============================================================ -->
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</style>
<div class="npxdi-hl" id="npxdi-highlights">
  <h3 class="npxdi-hl-h3">Points forts</h3>
  <div class="npxdi-hl-grid">
    <div class="npxdi-hl-card" role="button" tabindex="0" data-hl-open="a" aria-haspopup="dialog">
      <div class="npxdi-hl-t">Faciliter le déploiement d'applications HPC sur des supercalculateurs exascale à l'aide de gestionnaires de paquets</div>
      <div class="npxdi-hl-s">L'installation, la portabilité et la reproductibilité des applications parallèles sur les grandes plateformes informatiques constituent un défi de plus en plus important…</div>
      <span class="npxdi-hl-btn">Plus d'informations <span aria-hidden="true">→</span></span>
    </div>
    <div class="npxdi-hl-card" role="button" tabindex="0" data-hl-open="b" aria-haspopup="dialog">
      <div class="npxdi-hl-t">Vers l'exascale : faire évoluer à grande échelle les flux de travail liés aux problèmes inverses basés sur l'IA sur plusieurs nœuds et GPU grâce à une API simple.</div>
      <div class="npxdi-hl-s">Développer des solveurs inverses à grande échelle basés sur l'IA ainsi que des tests de référence pour des applications telles que la tomodensitométrie, la cryo-microscopie électronique et la radioastronomie.</div>
      <span class="npxdi-hl-btn">Plus d'informations <span aria-hidden="true">→</span></span>
    </div>
  </div>

  <!-- ===== HTML caché traduisible (cloné à l'ouverture, pattern Exa-DoST) ===== -->
  <div hidden="" data-hl="a">
    <p class="npxdi-hl-kicker">Mettre en évidence</p>
    <h2 class="npxdi-hl-h2">Faciliter le déploiement d'applications HPC sur des supercalculateurs exascale à l'aide de gestionnaires de paquets</h2>
    <p class="npxdi-hl-p">L'installation, la portabilité et la reproductibilité des applications parallèles sur les grandes plateformes de calcul sont devenues de plus en plus complexes en raison de la complexité croissante des logiciels, des dépendances étendues (par exemple, les frameworks d'IA) et des architectures matérielles hétérogènes. Les approches actuelles, fondées sur des modules et utilisées sur la plupart des supercalculateurs, atteignent leurs limites, ce qui se traduit par des tâches chronophages tant pour les équipes d’assistance que pour les utilisateurs. Les gestionnaires de paquets logiciels modernes ouvrent la voie à un nouveau paradigme dans lequel les utilisateurs peuvent gérer directement l’installation de leurs logiciels et de leurs dépendances.  </p>
    <p class="npxdi-hl-p">Dans le cadre des efforts d'intégration du logiciel NumPEx, Exa-DI recommande aux utilisateurs de recourir à des gestionnaires de paquets modernes afin d'exercer un contrôle direct et précis sur leur pile logicielle, en mettant particulièrement l'accent sur Guix et Spack. Dans ce contexte, une série de formations et d’actions d’accompagnement axées sur Guix, Spack et les spécificités du déploiement de logiciels sur les supercalculateurs a donc été organisée et dispensée par le Work Package 3 d’Exa-DI. Depuis 2025, 7 tutoriels NumPEx consacrés à Spack et Guix, destinés aux débutants et aux utilisateurs avancés, ont été dispensés et 200 personnes ont été formées.  </p>
    <p class="npxdi-hl-p">L'adoption de nouvelles méthodes de déploiement de logiciels prend du temps. Exa-DI s'efforce en permanence d'aider les utilisateurs en proposant des formations et une assistance, en créant des paquets logiciels, en améliorant les outils et en nouant des partenariats avec des centres de supercalcul afin d'optimiser l'expérience utilisateur. L’ambition est de faire en sorte que toutes les bibliothèques liées à NUMPEX soient packagées avec Guix et Spack, d’intégrer le déploiement basé sur Guix/Spack à la panoplie d’outils de chaque développeur, et de collaborer avec les centres de calcul afin de rendre le déploiement de logiciels au niveau utilisateur, basé sur Guix/Spack, aussi fluide que possible.  </p>
    <p class="npxdi-hl-strong">Pour plus d'informations :</p>
    <div class="npxdi-hl-links">
      <a href="https://numpex-pc5.gitlabpages.inria.fr/tutorials/webinar/index.html" target="_blank" rel="noopener">Webinaires et formations <span aria-hidden="true">↗</span></a>
      <a href="https://www.canal-u.tv/chaines/numpex" target="_blank" rel="noopener">NumPEx sur Canal-U <span aria-hidden="true">↗</span></a>
    </div>
  </div>
  <div hidden="" data-hl="b">
    <p class="npxdi-hl-kicker">Mettre en évidence</p>
    <h2 class="npxdi-hl-h2">Vers l'exascale : faire évoluer à grande échelle les flux de travail liés aux problèmes inverses basés sur l'IA sur plusieurs nœuds et GPU grâce à une API simple.</h2>
    <div class="npxdi-hl-team">Groupe de travail <b>Exa-DI WG4 :</b> Benoît Malézieux (Exa-DI), Thomas Moreau (Exa-DI), Triem Phan (Exa-DI), Julian Tachella (CNRS / DeepInv), Romain Vo (ENS Lyon / DeepInv)</div>
    <ul class="npxdi-hl-fields">
      <li><b>Objectif de recherche :</b> Développer des solveurs inverses à grande échelle basés sur l'IA ainsi que des tests de référence pour des applications telles que la tomodensitométrie, la cryo-microscopie électronique et la radioastronomie.</li>
      <li><b>Approche technique :</b> Permettre l'entraînement de réseaux « unrolled » sur des données à grande échelle grâce à un calcul multi-nœuds et multi-GPU, en utilisant la physique distribuée, un débrouilleur, des a priori et des gradients rétroactifs, le tout via une API simple dans la bibliothèque DeepInv.</li>
      <li><b>Réalisation majeure :</b> intégration réussie via une API simple dans la bibliothèque open source DeepInv, largement utilisée, et validation sur des problèmes de tomographie 3D à grande échelle (500³ voxels), jusqu’alors irréalisables pour la plupart des méthodes. Évaluation comparative réalisée dans le cadre du framework BenchOpt sur le jeu de données Jean Day à l'aide de 16 GPU H100 (10 s par échantillon). </li>
      <li><b>Impact de l'apprentissage à grande échelle :</b> il lève une contrainte majeure et ouvre un nouveau domaine de recherche à des échelles réalistes.</li>
      <li><b>Accessibilité :</b> Permet à une communauté plus large, au-delà des experts en calcul haute performance (HPC), d'accéder à l'apprentissage des problèmes inverses à grande échelle basé sur l'IA.</li>
      <li><b>Applications futures :</b> Cette méthode peut être étendue à d'autres modèles et à des applications à grande échelle, notamment dans le domaine de l'électrotomographie à grande échelle et de la radio-interférométrie.</li>
    </ul>
    <div class="npxdi-hl-figs">
      <img src="https://numpex.org/wp-content/uploads/sites/6/2026/08/exa-di-highlight-wg4-image1.png" alt="Indicateurs d'entraînement : perte par époque et PSNR par époque, courbes d'entraînement et de validation">
      <img src="https://numpex.org/wp-content/uploads/sites/6/2026/08/exa-di-highlight-wg4-image2.png" alt="Comparaison qualitative des reconstructions : données de référence (GT), modèle de base clairsemé, sortie d'un réseau non entraîné et prédiction d'un réseau entraîné et « unrolled »">
      <p class="npxdi-hl-cap">En haut : Courbes d'apprentissage illustrant le comportement de convergence de la méthode « unrolled » distribuée proposée. En bas : Comparaison qualitative des reconstructions, comprenant la référence (GT), la base de référence clairsemée, la sortie du réseau non entraîné et celle du réseau « unrolled » entraîné (3 itérations). Notre infrastructure à grande échelle permet un apprentissage efficace de cette architecture « unrolled » sur 16 GPU H100, ce qui rend possible le traitement de données tomographiques à haute résolution (500³ voxels).  </p>
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