{"id":2416,"date":"2025-10-16T12:08:03","date_gmt":"2025-10-16T10:08:03","guid":{"rendered":"https:\/\/numpex.org\/lia-au-service-de-la-science\/"},"modified":"2026-08-10T18:11:15","modified_gmt":"2026-08-10T16:11:15","slug":"lia-au-service-de-la-science","status":"publish","type":"page","link":"https:\/\/numpex.org\/fr\/lia-au-service-de-la-science\/","title":{"rendered":"L’IA au service de la science"},"content":{"rendered":"

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uncode_shortcode_id=\u00a0\u00bb191166″ el_id=\u00a0\u00bbintro-exa-atow-en\u00a0\u00bb]Bienvenue dans notre groupe de travail sur l’IA[\/vc_custom_heading][vc_column_text text_lead=\u00a0\u00bbyes\u00a0\u00bb uncode_shortcode_id=\u00a0\u00bb803044″]Le calcul \u00e0 haute performance (HPC) est un \u00e9cosyst\u00e8me qui relie de nombreuses communaut\u00e9s scientifiques, des math\u00e9matiques et de l’ing\u00e9nierie aux sciences exp\u00e9rimentales et \u00e0 l’informatique. Il fournit les outils et les environnements n\u00e9cessaires pour aborder les nouvelles architectures informatiques hybrides et acc\u00e9l\u00e9r\u00e9es qui alimenteront la recherche future. Les progr\u00e8s r\u00e9cents de l’IA changent la donne pour la science, en offrant de nouvelles fa\u00e7ons d’analyser les donn\u00e9es et d’am\u00e9liorer les simulations gr\u00e2ce \u00e0 une int\u00e9gration pouss\u00e9e de l’IA et du calcul intensif. Dans ce contexte, notre programme de recherche NumPEx vise \u00e0 r\u00e9unir l’IA et le HPC \u00e0 grande \u00e9chelle en d\u00e9veloppant des logiciels pr\u00eats pour Exascale, des m\u00e9thodes d’analyse de donn\u00e9es bas\u00e9es sur l’IA et des capacit\u00e9s d’entra\u00eenement de grands mod\u00e8les. Ce faisant, NumPEx acc\u00e9l\u00e9rera l’utilisation de l’IA pour la science et renforcera les collaborations entre les communaut\u00e9s de recherche nationales et internationales. <\/strong><\/p>\n

[\/vc_column_text][vc_column_text text_lead=\u00a0\u00bbyes\u00a0\u00bb uncode_shortcode_id=\u00a0\u00bb131918″]Le calcul de haute performance (HPC) est un \u00e9cosyst\u00e8me \u00e0 l’intersection de vastes communaut\u00e9s. Il rel\u00e8ve les d\u00e9fis m\u00e9thodologiques, algorithmiques, logiciels et de portabilit\u00e9 pour exploiter efficacement les nouvelles architectures de calcul hybrides, h\u00e9t\u00e9rog\u00e8nes et hautement acc\u00e9l\u00e9r\u00e9es, qui seront les architectures de demain dans les centres r\u00e9gionaux et nationaux. Le calcul haute performance vise \u00e0 fournir un environnement informatique qui acc\u00e9l\u00e8re la recherche dans de nombreuses communaut\u00e9s de sciences appliqu\u00e9es et industrielles, dans des domaines tels que l’astrophysique, la physique des hautes \u00e9nergies, le climat et l’environnement, la biologie et la sant\u00e9 num\u00e9rique, la neuroimagerie, la chimie physique, les sciences humaines et sociales, l’a\u00e9ronautique, la production\/transition d’\u00e9nergie, pour n’en citer que quelques-uns. Dans la plupart de ces domaines, le d\u00e9veloppement r\u00e9cent de l’IA est consid\u00e9r\u00e9 comme un facteur de changement pour mieux int\u00e9grer et analyser les donn\u00e9es exp\u00e9rimentales ainsi que pour am\u00e9liorer consid\u00e9rablement les simulations num\u00e9riques gr\u00e2ce \u00e0 l’hybridation profonde IA\/HPC, repoussant ainsi de nouvelles fronti\u00e8res afin d’acc\u00e9l\u00e9rer la recherche. Cependant, l’utilisation de l’IA dans ces domaines reste souvent au niveau de la preuve du concept et n\u00e9cessite donc un bond en avant avec une mise en \u0153uvre \u00e0 l’\u00e9chelle et une validation scientifique compl\u00e8te. <\/p>\n

Les r\u00e9centes avanc\u00e9es dans le domaine de l’IA ont montr\u00e9 que les grands ensembles de donn\u00e9es disponibles et l’\u00e9norme puissance de calcul sont les \u00e9l\u00e9ments essentiels n\u00e9cessaires au d\u00e9veloppement de mod\u00e8les puissants et flexibles pour le traitement du langage naturel et des images. Le positionnement du calcul intensif au carrefour de divers domaines scientifiques et l’expertise de la communaut\u00e9 dans la gestion de grandes infrastructures de calcul et de donn\u00e9es en font le terrain id\u00e9al pour d\u00e9velopper l’utilisation novatrice de l’IA pour la science. En effet, l’\u00e9cosyst\u00e8me HPC rassemble des donn\u00e9es exp\u00e9rimentales et d’observation massives, de grandes ressources de calcul et des praticiens experts, qui sont essentiels \u00e0 l’av\u00e8nement de mod\u00e8les d’IA puissants pour aborder des questions scientifiques complexes. De plus, l’expertise de la communaut\u00e9 HPC dans l’exploitation efficace de grandes infrastructures de calcul offre \u00e9galement la possibilit\u00e9 d’am\u00e9liorer les pratiques d’IA pour former de grands mod\u00e8les.[\/vc_column_text][vc_row_inner row_inner_height_percent=\u00a0\u00bb0″ overlay_alpha=\u00a0\u00bb50″ gutter_size=\u00a0\u00bb3″ shift_y=\u00a0\u00bb0″ z_index=\u00a0\u00bb0″ limit_content=\u00a0\u00bb\u00a0\u00bb uncode_shortcode_id=\u00a0\u00bb144338″ el_id=\u00a0\u00bbour-code-of-conduct-en\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-our-actions\u00a0\u00bb][vc_custom_heading heading_semantic=\u00a0\u00bbh1″ text_size=\u00a0\u00bbh3″ uncode_shortcode_id=\u00a0\u00bb157537″]Nos actions[\/vc_custom_heading][\/vc_column_inner][\/vc_row_inner][vc_column_text uncode_shortcode_id=\u00a0\u00bb172883″]Dans ce contexte, l’initiative NumPEx vise \u00e0 faire progresser l’utilisation conjointe de l’IA et du calcul intensif dans plusieurs directions :[\/vc_column_text][vc_accordion gutter_simple=\u00a0\u00bb0″ uncode_shortcode_id=\u00a0\u00bb810290″][vc_accordion_tab gutter_size=\u00a0\u00bb2″ column_padding=\u00a0\u00bb2″ title=\u00a0\u00bbHybridation du calcul intensif et de l'intelligence artificielle\u00a0\u00bb tab_id=\u00a0\u00bb1763469889-1-21″][vc_column_text uncode_shortcode_id=\u00a0\u00bb184117″]Avec la fin de la loi de Moore, une direction de recherche \u00e9mergente vise \u00e0 d\u00e9velopper des approches hybrides bas\u00e9es sur l’IA pour acc\u00e9l\u00e9rer certaines parties des applications de calcul scientifique. Cette hybridation peut prendre plusieurs formes, du d\u00e9veloppement de codes de simulation incluant des mod\u00e8les d’IA inform\u00e9s par la physique, \u00e0 l’optimisation des temps d’ex\u00e9cution bas\u00e9s sur des mod\u00e8les inform\u00e9s par les logs, ou \u00e0 la r\u00e9duction des donn\u00e9es d’observation et des simulations num\u00e9riques \u00e0 l’\u0153uvre dans les mod\u00e8les r\u00e9duits et les jumeaux num\u00e9riques. \u00c0 cette fin, NumPEx vise \u00e0 faire progresser une pile logicielle pr\u00eate pour Exascale, int\u00e9grant des solutions d’IA qui r\u00e9pondent aux besoins des communaut\u00e9s d’application. <\/p>\n

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[\/vc_column_text][\/vc_accordion_tab][vc_accordion_tab gutter_size=\u00a0\u00bb2″ column_padding=\u00a0\u00bb2″ title=\u00a0\u00bbAnalyse des donn\u00e9es HPC\u00a0\u00bb tab_id=\u00a0\u00bb1763469889-2-44″][vc_column_text uncode_shortcode_id=\u00a0\u00bb134043″]Les donn\u00e9es massives produites par le calcul scientifique avec HPC soul\u00e8vent \u00e9galement des d\u00e9fis particuliers pour l’application de mod\u00e8les d’IA, car elles ne peuvent souvent pas \u00eatre stock\u00e9es et collect\u00e9es sur le syst\u00e8me de calcul. Cela n\u00e9cessite des m\u00e9thodologies d’IA sp\u00e9cifiques, capables d’analyser ou d’assimiler des donn\u00e9es massives \u00e0 l’\u00e9chelle, qu’elles proviennent de solutions num\u00e9riques ou de donn\u00e9es d’observation, avec des solutions bas\u00e9es sur l’IA pour l’analyse de donn\u00e9es in-stream et in-situ, l’am\u00e9lioration de l’IO, ou le checkpointing, pour n’en citer que quelques-unes. Dans le cadre de NumPEx, les flux de travail assist\u00e9s par l’IA seront \u00e9tudi\u00e9s pour la gestion des donn\u00e9es distribu\u00e9es et le calcul distribu\u00e9.[\/vc_column_text][\/vc_accordion_tab][vc_accordion_tab gutter_size=\u00a0\u00bb2″ column_padding=\u00a0\u00bb2″ title=\u00a0\u00bbL'IA \u00e0 grande \u00e9chelle \u00e0 l'\u00e8re Exascale et post-Exascale\u00a0\u00bb tab_id=\u00a0\u00bb1763470057815-2-8″][vc_column_text uncode_shortcode_id=\u00a0\u00bb185067″]Ces derni\u00e8res ann\u00e9es ont \u00e9t\u00e9 marqu\u00e9es par la (r)\u00e9volution tr\u00e8s rapide de mod\u00e8les extr\u00eamement volumineux, notamment de grands mod\u00e8les linguistiques ou, plus g\u00e9n\u00e9ralement, de mod\u00e8les fondamentaux tels que les mod\u00e8les de diffusion, les mod\u00e8les g\u00e9n\u00e9ratifs multimodaux, etc. Ces mod\u00e8les n\u00e9cessitent l’utilisation intensive de superordinateurs
\net d’\u00e9normes infrastructures de stockage de donn\u00e9es pour apprendre des mod\u00e8les comportant des milliards ou des trillions de param\u00e8tres \u00e0 partir de p\u00e9taoctets de donn\u00e9es. Cela pose des d\u00e9fis en mati\u00e8re de gestion des donn\u00e9es et de calcul sur des syst\u00e8mes de niveau Exascale, avec une utilisation massive d’architectures acc\u00e9l\u00e9r\u00e9es h\u00e9t\u00e9rog\u00e8nes. \u00c0 cet \u00e9gard, les contributions de NumPEx \u00e0 la pile logicielle Exascale couvriront davantage les biblioth\u00e8ques de composants bas\u00e9s sur l’IA, l’interface entre les biblioth\u00e8ques HPC traditionnelles et les cadres standards d’IA et d’apprentissage automatique (par exemple PyTorch, Tensorflow, Scikit-Learn, etc.). NumPEx est la pierre angulaire de l’\u00e9cosyst\u00e8me Exascale, entre les communaut\u00e9s d’application, les fournisseurs HPC et la communaut\u00e9 de recherche en math\u00e9matiques appliqu\u00e9es et en informatique.[\/vc_column_text][\/vc_accordion_tab][\/vc_accordion][vc_column_text uncode_shortcode_id=\u00a0\u00bb310370″]\u00c0 cet \u00e9gard, NumPEx contribuera \u00e0 :<\/p>\n

Acc\u00e9l\u00e9rer l’IA pour la science<\/strong><\/span><\/h3>\n

NumPEx vise \u00e0 acc\u00e9l\u00e9rer la diffusion et l’application de l’IA \u00e0 tous les stades, en mettant fortement l’accent sur l’IA pour les applications scientifiques et d’ing\u00e9nierie. \u00c0 cette fin, il aidera les communaut\u00e9s scientifiques et techniques \u00e0 acc\u00e9l\u00e9rer l’int\u00e9gration de l’IA \u00e0 grande \u00e9chelle gr\u00e2ce \u00e0 la co-conception centr\u00e9e sur l’IA. Au sein des communaut\u00e9s d’utilisateurs scientifiques et d’ing\u00e9nierie Exascale, les utilisations de l’IA couvrent un spectre tr\u00e8s large, allant des applications proches de la production \u00e0 faible maturit\u00e9 \u00e0 la preuve de concept \u00e0 faible maturit\u00e9, voire pas d’utilisation du tout mais une int\u00e9gration planifi\u00e9e. Par cons\u00e9quent, NumPEx aidera davantage les communaut\u00e9s d’utilisateurs \u00e0 int\u00e9grer des solutions bas\u00e9es sur l’IA et \u00e0 les d\u00e9ployer sur des plateformes Exascale. \u00c0 cette fin, NumPEx s’appuiera sur une activit\u00e9 de co-conception centr\u00e9e sur l’IA, en se concentrant sur des cas d’utilisation d’applications flexibles partag\u00e9es. Comprenant des donn\u00e9es et des mesures bien d\u00e9finies d\u00e9pendant de l’application, ces cas d’utilisation constitueront le banc d’essai n\u00e9cessaire pour tester et \u00e9valuer les solutions bas\u00e9es sur l’IA d\u00e9velopp\u00e9es dans le cadre de NumPEx. Dans ce contexte, le NumPEx s’engagera fortement dans le d\u00e9veloppement de la science ouverte (source et donn\u00e9es). <\/p>\n

Combler le foss\u00e9 entre les communaut\u00e9s des applications et de l’intelligence artificielle de base<\/strong><\/span><\/h3>\n

L’objectif de NumPEx est de jeter des ponts entre la communaut\u00e9 traditionnelle du calcul intensif et de l’informatique et les principales communaut\u00e9s de l’IA, des math\u00e9matiques appliqu\u00e9es \u00e0 la vision par ordinateur ou au traitement des signaux. \u00c0 cette fin, le NumPEx participera et organisera des \u00e9v\u00e9nements scientifiques interdisciplinaires ax\u00e9s sur l’IA pour la science (par exemple, des programmes th\u00e9matiques \u00e0 long terme, des \u00e9coles). Le succ\u00e8s de l’IA repose en grande partie sur les d\u00e9fis ouverts massifs 1, qui exposent les besoins des communaut\u00e9s d’application \u00e0 un public beaucoup plus large, ce qui permet une d\u00e9couverte plus rapide. En s’appuyant sur l’activit\u00e9 de co-conception, NumPEx encourage les d\u00e9fis ouverts, en se concentrant sur des cas d’utilisation d’applications cl\u00e9s. <\/p>\n

Favoriser les collaborations et les initiatives au niveau national et international<\/strong><\/span><\/h3>\n

L’IA est un \u00e9cosyst\u00e8me complexe et en \u00e9volution tr\u00e8s rapide, qui n\u00e9cessite des collaborations interdisciplinaires et internationales. NumPEx, \u00e0 travers son initiative internationale InPEx<\/a>, est parfaitement connect\u00e9 \u00e0 la communaut\u00e9 Exascale internationale, et plusieurs
\ndes organismes pilotes sont membres de TPC. Dans ce contexte, le NumPEx participera activement \u00e0 des projets nationaux et europ\u00e9ens centr\u00e9s sur l’IA, en mettant l’accent sur le d\u00e9veloppement de grands mod\u00e8les fondamentaux \u00e0 l’\u00e9chelle pour la science. NumPEx d\u00e9veloppera et renforcera les collaborations internationales en participant \u00e0 des initiatives transnationales telles que le Trillion Parameter Consortium (TPC).[\/vc_column_text][vc_row_inner row_inner_height_percent=\u00a0\u00bb0″ overlay_alpha=\u00a0\u00bb50″ gutter_size=\u00a0\u00bb3″ shift_y=\u00a0\u00bb0″ z_index=\u00a0\u00bb0″ limit_content=\u00a0\u00bb\u00a0\u00bb uncode_shortcode_id=\u00a0\u00bb144338″ el_id=\u00a0\u00bbour-code-of-conduct-en\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-contact\u00a0\u00bb][vc_custom_heading heading_semantic=\u00a0\u00bbh1″ text_size=\u00a0\u00bbh3″ uncode_shortcode_id=\u00a0\u00bb486342″]Contacts[\/vc_custom_heading][\/vc_column_inner][\/vc_row_inner][vc_column_text uncode_shortcode_id=\u00a0\u00bb202726″]Pour plus d’informations sur le groupe de travail \u00ab\u00a0AI for science\u00a0\u00bb et ses activit\u00e9s, veuillez contacter J\u00e9r\u00f4me Bobin<\/a>, Thomas Moreau<\/a> et Emmanuel Franck<\/a>.<\/p>\n

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