{"id":183381,"date":"2026-09-17T16:54:32","date_gmt":"2026-09-17T14:54:32","guid":{"rendered":"https:\/\/prohoster.info\/blog\/news\/nvidia-predstavila-cuda-rust-dlya-razrabotki-gpu-yader-na-yazyke-rust"},"modified":"2026-09-17T16:54:36","modified_gmt":"2026-09-17T14:54:36","slug":"nvidia-introduces-cuda-rust-for-developing-gpu-kernels-in-rust","status":"publish","type":"post","link":"https:\/\/prohoster.info\/fr\/blog\/news\/nvidia-introduces-cuda-rust-for-developing-gpu-kernels-in-rust","title":{"rendered":"NVIDIA a pr\u00e9sent\u00e9 CUDA Rust pour le d\u00e9veloppement de noyaux GPU en langage Rust.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>La soci\u00e9t\u00e9 NVIDIA a annonc\u00e9 le d\u00e9veloppement d'un outil CUDA Rust, permettant d'utiliser le langage Rust pour d\u00e9velopper des noyaux ex\u00e9cut\u00e9s sur GPU. Cet outil assure la s\u00e9curit\u00e9 de la gestion de la m\u00e9moire au moment de la compilation et pr\u00e9vient les \u00e9tats de concurrence. L'ann\u00e9e prochaine, CUDA Rust devrait atteindre un niveau ad\u00e9quat pour le d\u00e9veloppement de projets op\u00e9rationnels, comparable aux outils CUDA C++ et CUDA Python.       <\/p>\n<p>L'outil CUDA Rust prend en charge deux mod\u00e8les de d\u00e9veloppement de noyaux s'ex\u00e9cutant simultan\u00e9ment : SIMT (Single Instruction, Multiple Threads) et Tile. Le mod\u00e8le SIMT permet de d\u00e9finir la logique d'un seul flux \u00e0 un niveau bas, de lancer des milliers de ces flux et de les g\u00e9rer. Le mod\u00e8le Tile propose un niveau d'abstraction plus \u00e9lev\u00e9, o\u00f9 au lieu de g\u00e9rer explicitement des flux individuels, des algorithmes d'action sur des blocs de donn\u00e9es (tile) sont d\u00e9finis, le compilateur Tile IR prenant en charge toutes les manipulations de flux, la synchronisation d'acc\u00e8s, la gestion de la m\u00e9moire et la r\u00e9partition des donn\u00e9es entre les noyaux tensoriels.       <\/p>\n<p>Pour le d\u00e9veloppement en Rust en utilisant le mod\u00e8le SIMT, un compilateur cuda-oxide est en cours de d\u00e9veloppement, permettant de compiler du code en Rust, utilisant le syst\u00e8me de types standard et le mod\u00e8le de possession de Rust, directement en instructions pour ex\u00e9cution dans <a class=\"wpil_keyword_link\" href=\"https:\/\/prohoster.info\/fr\/vps\/abuzoustojchivye-vps\/\"   title=\"une machine virtuelle\" data-wpil-keyword-link=\"linked\">une machine virtuelle<\/a> CUDA PTX (Parallel Thread Execution). Les noyaux pour GPU sont cr\u00e9\u00e9s en Rust ordinaire, mais s'ex\u00e9cutent dans un environnement no_std et ne peuvent utiliser que les fonctions de la biblioth\u00e8que libcore et des abstractions Rust sp\u00e9cialis\u00e9es, sans acc\u00e8s \u00e0 la biblioth\u00e8que standard Rust (libstd).   <\/p>\n<p>Dans les noyaux CUDA en Rust, l'utilisation de la protection par le biais du syst\u00e8me de types (safe), l'utilisation de blocs unsafe et l'acc\u00e8s \u00e0 des instructions mat\u00e9rielles de bas niveau sont autoris\u00e9s. Pour garantir la s\u00e9curit\u00e9, un type DisjointSlice est propos\u00e9, garantissant que chaque thread a un acc\u00e8s exclusif uniquement \u00e0 ses propres donn\u00e9es. Le code cuda-oxide est distribu\u00e9 sous la licence Apache 2.0.    <\/p>\n<p>Pour appliquer le mod\u00e8le Tail, une biblioth\u00e8que appel\u00e9e cutile-rs est propos\u00e9e, permettant d'utiliser le langage idiomatique Rust pour cr\u00e9er du code compil\u00e9 directement dans des noyaux CUDA. Cutile-rs applique les strictes r\u00e8gles de propri\u00e9t\u00e9 et d'emprunt propos\u00e9es par Rust au code s'ex\u00e9cutant sur le GPU. Le noyau est con\u00e7u comme un programme monothread fonctionnant avec un bloc de donn\u00e9es, et le compilateur d\u00e9coupe les calculs en flux et assure leur synchronisation. Les flux ont acc\u00e8s en commun \u00e0 des tenseurs immuables, et l'acc\u00e8s s\u00e9curis\u00e9 aux tenseurs mutables est r\u00e9alis\u00e9 en les divisant en blocs non chevauchants. Le code de cutile-rs est distribu\u00e9 sous la licence Apache 2.0.<br \/>\n<br \/>Source : <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=66295\">opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f NVIDIA \u043e\u0431\u044a\u044f\u0432\u0438\u043b\u0430 \u043e \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u0438 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u044f CUDA Rust, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0435\u0433\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u044f\u0437\u044b\u043a Rust \u0434\u043b\u044f \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u044f\u0434\u0435\u0440, \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u043c\u044b\u0445 \u043d\u0430 \u0441\u0442\u043e\u0440\u043e\u043d\u0435 GPU. \u0418\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 \u043e\u0431\u0435\u0441\u043f\u0435\u0447\u0438\u0432\u0430\u0435\u0442 \u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u044c \u0440\u0430\u0431\u043e\u0442\u044b \u0441 \u043f\u0430\u043c\u044f\u0442\u044c\u044e \u043d\u0430 \u044d\u0442\u0430\u043f\u0435 \u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0446\u0438\u0438 \u0438 \u043f\u0440\u0435\u0434\u043e\u0442\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u0432\u043e\u0437\u043d\u0438\u043a\u043d\u043e\u0432\u0435\u043d\u0438\u0435 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u0439 \u0433\u043e\u043d\u043a\u0438. \u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c \u0433\u043e\u0434\u0443 CUDA Rust \u043f\u043b\u0430\u043d\u0438\u0440\u0443\u044e\u0442 \u0434\u043e\u0432\u0435\u0441\u0442\u0438 \u0434\u043e \u0443\u0440\u043e\u0432\u043d\u044f, \u043f\u0440\u0438\u0433\u043e\u0434\u043d\u043e\u0433\u043e \u0434\u043b\u044f \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0440\u0430\u0431\u043e\u0447\u0438\u0445 \u043f\u0440\u043e\u0435\u043a\u0442\u043e\u0432, \u0430\u043d\u0430\u043b\u043e\u0433\u0438\u0447\u043d\u043e\u0433\u043e \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u044f\u043c CUDA C++ \u0438 CUDA Python. \u0418\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":10,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-183381","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f NVIDIA 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