{"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\/ro\/blog\/news\/nvidia-introduces-cuda-rust-for-developing-gpu-kernels-in-rust","title":{"rendered":"NVIDIA a prezentat CUDA Rust pentru dezvoltarea nucleelor GPU \u00een limbajul Rust","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Compania NVIDIA a anun\u021bat dezvoltarea instrumentelor CUDA Rust, care permit utilizarea limbajului Rust pentru dezvoltarea de nuclee care ruleaz\u0103 pe GPU. Instrumentele asigur\u0103 siguran\u021ba gestion\u0103rii memoriei \u00een timpul compil\u0103rii \u0219i previn apari\u021bia st\u0103rilor de concuren\u021b\u0103. \u00cen anul urm\u0103tor, CUDA Rust urmeaz\u0103 s\u0103 fie adus la un nivel adecvat pentru dezvoltarea proiectelor de lucru, similar instrumentelor CUDA C++ \u0219i CUDA Python.       <\/p>\n<p>Instrumentul CUDA Rust suport\u0103 dou\u0103 modele de dezvoltare a nucleelor executabile \u00een paralel \u2014 SIMT (Single Instruction, Multiple Threads) \u0219i Tile. Modelul SIMT permite definirea logicii unui singur fir la un nivel fundamental, lans\u00e2nd mii de astfel de fire \u0219i gestion\u00e2ndu-le. Modelul Tile ofer\u0103 un nivel de abstractizare mai \u00eenalt, \u00een care, \u00een loc de gestionarea explicita a firelor individuale, sunt definite algoritmi de ac\u021biune cu blocuri de date (tile), iar toate manevrele cu firele, sincronizarea accesului, gestionarea memoriei \u0219i distribu\u021bia datelor \u00eentre nucleele tensoriale sunt realizate de compilatorul Tile IR.       <\/p>\n<p>Pentru dezvoltarea \u00een limbajul Rust folosind modelul SIMT, se dezvolt\u0103 compilatorul cuda-oxide, care permite compilarea codului \u00een limbajul Rust, utiliz\u00e2nd sistemul standard de tipuri \u0219i modelul de proprietate Rust, direct \u00een instruc\u021biuni pentru a fi executate \u00een <a class=\"wpil_keyword_link\" href=\"https:\/\/prohoster.info\/ro\/vps\/abuzoustojchivye-vps\/\"   title=\"ma\u0219ina virtual\u0103\" data-wpil-keyword-link=\"linked\">ma\u0219ina virtual\u0103<\/a> CUDA PTX (Parallel Thread Execution). Nuclee pentru GPU sunt create \u00een Rust obi\u0219nuit, dar ruleaz\u0103 \u00eentr-un mediu no_std \u0219i pot utiliza doar func\u021bii din biblioteca libcore \u0219i abstra\u021bii specializate Rust, f\u0103r\u0103 acces la biblioteca standard Rust (libstd).   <\/p>\n<p>\u00cen nuclee CUDA scrise \u00een Rust, este permis\u0103 aplicarea protec\u021biei prin sistemul de tipuri (safe), utilizarea blocurilor unsafe \u0219i apelarea instruc\u021biunilor hardware la un nivel sc\u0103zut. Pentru a asigura siguran\u021ba, se propune tipul DisjointSlice, care garanteaz\u0103 c\u0103 fiecare fir are acces exclusiv doar la datele sale. Codul cuda-oxide este distribuit sub licen\u021ba Apache 2.0.    <\/p>\n<p>Pentru aplicarea modelului Tail, se propune biblioteca cutile-rs, care permite utilizarea unui limbaj idiomatic Rust pentru a crea cod ce se compileaz\u0103 direct \u00een nuclee CUDA. Cutile-rs aplic\u0103 regulile stricte de proprietate \u0219i \u00eemprumut propuse \u00een Rust pentru codul executat pe GPU. Nucleul este structurat ca un program unifilar care lucreaz\u0103 cu un bloc de date, iar compilatorul \u00ee\u0219i \u00eemparte automat calculele \u00een fire \u0219i asigur\u0103 sincronizarea acestora. Firele au acces comun la tensori imuabili, iar pentru accesul sigur la tensori modificabili, ace\u0219tia sunt \u00eemp\u0103r\u021bi\u021bi \u00een blocuri neintersectante. Codul cutile-rs este distribuit sub licen\u021ba Apache 2.0.<br \/>\n<br \/>Sursa: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=66295\">opennet.ro<\/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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