{"id":113552,"date":"2024-02-12T22:02:43","date_gmt":"2024-02-12T20:02:45","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/opublikovan-instrumentarij-zluda-pozvolyayushhij-zapuskat-cuda-prilozheniya-na-gpu-amd"},"modified":"2024-02-12T22:02:43","modified_gmt":"2024-02-12T20:02:45","slug":"opublikovan-instrumentarij-zluda-pozvolyayushhij-zapuskat-cuda-prilozheniya-na-gpu-amd","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sq\/blog\/novosti-interneta\/opublikovan-instrumentarij-zluda-pozvolyayushhij-zapuskat-cuda-prilozheniya-na-gpu-amd","title":{"rendered":"ZLUDA ka publikuar mjete q\u00eb mund\u00ebsojn\u00eb ekzekutimin e aplikacioneve CUDA n\u00eb GPU-t\u00eb AMD","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Projekti ZLUDA ka p\u00ebrgatitur nj\u00eb zbatim t\u00eb hapur t\u00eb teknologjis\u00eb CUDA p\u00ebr GPU-t\u00eb AMD, e cila lejon ekzekutimin e aplikacioneve CUDA t\u00eb pamodifikuara me performanc\u00eb af\u00ebr atyre t\u00eb aplikacioneve q\u00eb ekzekutohen pa nd\u00ebrfaqe. Mjeti i publikuar siguron p\u00ebrputhshm\u00ebrin\u00eb binar\u00eb me aplikacionet CUDA ekzistuese, t\u00eb nd\u00ebrtuara me ndihm\u00ebn e kompilerit CUDA p\u00ebr GPU-t\u00eb NVIDIA. Zbatimi operon mbi p\u00ebrb\u00ebrjen ROCm t\u00eb zhvilluar nga AMD dhe runtime HIP (Heterogeneous-computing Interface for Portability). Kodi i projektit \u00ebsht\u00eb shkruar n\u00eb gjuh\u00ebn Rust dhe shp\u00ebrndahet n\u00ebn licencat MIT dhe Apache 2.0. Mb\u00ebshtetet puna n\u00eb Linux dhe Windows. <\/p>\n

Sistemi p\u00ebr organizimin e pun\u00ebs s\u00eb CUDA n\u00eb sistemet me GPU AMD \u00ebsht\u00eb zhvilluar n\u00eb dy vitet e fundit, por projekti ka nj\u00eb histori m\u00eb t\u00eb gjat\u00eb dhe fillimisht \u00ebsht\u00eb krijuar p\u00ebr t\u00eb siguruar funksionimin e CUDA n\u00eb GPU Intel. Ndryshimi n\u00eb politik\u00ebn e mb\u00ebshtetjes s\u00eb GPU-ve shpjegohet me faktin se n\u00eb fillim zhvilluesi ZLUDA ishte nj\u00eb punonj\u00ebs i Intel, por n\u00eb vitin 2021 kjo kompani e vler\u00ebsoi ofrimin e mund\u00ebsis\u00eb p\u00ebr t\u00eb ekzekutuar aplikacionet CUDA n\u00eb GPU Intel si t\u00eb pap\u00ebrshtatshme p\u00ebr biznesin dhe nuk e vazhdoi zhvillimin e nism\u00ebs. <\/p>\n

N\u00eb fillim t\u00eb vitit 2022, zhvilluesi u largua nga Intel dhe n\u00ebnshkroi nj\u00eb kontrat\u00eb me kompanin\u00eb AMD p\u00ebr t\u00eb zhvilluar nj\u00eb shtres\u00eb p\u00ebr p\u00ebrputhshm\u00ebrin\u00eb me CUDA. Kompania AMD k\u00ebrkoi gjat\u00eb zhvillimit q\u00eb t\u00eb mos publikohej interesi i AMD p\u00ebr projektin ZLUDA dhe t\u00eb mos b\u00ebheshin komit\u00eb n\u00eb depozitarin publik t\u00eb ZLUDA. Pas dy vjet\u00ebsh, kompania AMD vendosi q\u00eb nisja e aplikacioneve CUDA n\u00eb GPU-n\u00eb AMD nuk p\u00ebrfaq\u00ebsonte interes t\u00eb biznesit, gj\u00eb q\u00eb sipas kushtit t\u00eb kontrat\u00ebs i lejoi zhvilluesit t\u00eb publikohej arritjet e tij. Duke qen\u00eb se prodhuesit e GPU-s\u00eb ndaluan financimin e projektit, fati i tij tani varet nga interesi i komunitetit dhe pranimi i propozimeve p\u00ebr bashk\u00ebpunim nga kompani t\u00eb tjera. Pa mb\u00ebshtetje t\u00eb jashtme, projekti do t\u00eb mund t\u00eb zhvillohej vet\u00ebm n\u00eb drejtimet q\u00eb jan\u00eb interesante p\u00ebr autorin personalisht, si DLSS (Deep Learning Super Sampling). <\/p>\n

N\u00eb form\u00ebn aktuale, niveli i cil\u00ebsis\u00eb s\u00eb realizimit vler\u00ebsohet si nj\u00eb version alfa. Megjithat\u00eb, ZLUDA tashm\u00eb mund t\u00eb p\u00ebrdoret p\u00ebr ekzekutimin e shum\u00eb aplikacioneve CUDA, duke p\u00ebrfshir\u00eb Geekbench, 3DF Zephyr, Blender, Reality Capture, LAMMPS, NAMD, waifu2x, OpenFOAM dhe Arnold. Mb\u00ebshtetje minimale \u00ebsht\u00eb e garantuar p\u00ebr primitive dhe biblioteka cuDNN, cuBLAS, cuSPARSE, cuFFT, NCCL dhe NVML. <\/p>\n

L\u00ebshimi i par\u00eb i aplikacioneve CUDA n\u00ebn ZLUDA ndodh me vonesa t\u00eb dukshme, p\u00ebr shkak se ZLUDA kryen kompilimin e kodit GPU. N\u00eb l\u00ebshimet e m\u00ebpasshme, kjo vones\u00eb nuk \u00ebsht\u00eb e pranishme, pasi kodi i kompiluar ruhet n\u00eb cache. Gjat\u00eb ekzekutimit t\u00eb kodit t\u00eb kompiluar, performanca \u00ebsht\u00eb e af\u00ebrt me at\u00eb natyrore. Kur ekzekutohej paketa Geekbench n\u00eb GPU-n\u00eb AMD Radeon 6800 XT, varianti i grupit t\u00eb testeve p\u00ebr CUDA, i realizuar me ZLUDA, tregoi nj\u00eb performanc\u00eb t\u00eb dukshme m\u00eb t\u00eb lart\u00eb se varianti i bazuar n\u00eb OpenCL.

\"ZLUDA<\/center> <\/p>\n

\u041f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430 \u043e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u0433\u043e CUDA Driver API \u0438 \u0438\u0437\u0443\u0447\u0435\u043d\u043d\u043e\u0439 \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u043e\u0431\u0440\u0430\u0442\u043d\u043e\u0433\u043e \u0438\u043d\u0436\u0438\u043d\u0438\u0440\u0438\u043d\u0433\u0430 \u0447\u0430\u0441\u0442\u0438 \u043d\u0435\u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0433\u043e API CUDA \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u043d\u0430 \u0432 ZLUDA \u0447\u0435\u0440\u0435\u0437 \u0437\u0430\u043c\u0435\u043d\u0443 \u0432\u044b\u0437\u043e\u0432\u043e\u0432 \u0444\u0443\u043d\u043a\u0446\u0438\u0439 \u043d\u0430 \u0430\u043d\u0430\u043b\u043e\u0433\u0438\u0447\u043d\u044b\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438, \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u043c\u044b\u0435 \u0432 HIP runtime, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0432\u043e \u043c\u043d\u043e\u0433\u043e\u043c \u043f\u043e\u0445\u043e\u0434\u0438\u0442 \u043d\u0430 CUDA. \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0444\u0443\u043d\u043a\u0446\u0438\u044f cuDeviceGetAttribute() \u0437\u0430\u043c\u0435\u043d\u044f\u0435\u0442\u0441\u044f \u043d\u0430 hipDeviceGetAttribute(). \u041f\u043e\u0445\u043e\u0436\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u043e\u0431\u0435\u0441\u043f\u0435\u0447\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u0438 \u0441\u043e\u0432\u043c\u0435\u0441\u0442\u0438\u043c\u043e\u0441\u0442\u044c \u0441 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430\u043c\u0438 NVIDIA, \u0442\u0430\u043a\u0438\u043c\u0438 \u043a\u0430\u043a NVML, cuBLAS \u0438 cuSPARSE, — \u0434\u043b\u044f \u043f\u043e\u0434\u043e\u0431\u043d\u044b\u0445 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a \u0432 ZLUDA \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u044e\u0442\u0441\u044f \u0442\u0440\u0430\u043d\u0441\u043b\u0438\u0440\u0443\u044e\u0449\u0438\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u0441 \u0442\u0435\u043c \u0436\u0435 \u0438\u043c\u0435\u043d\u0435\u043c \u0438 \u0442\u0435\u043c \u0436\u0435 \u043d\u0430\u0431\u043e\u0440\u043e\u043c \u0444\u0443\u043d\u043a\u0446\u0438\u0439, \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u043d\u044b\u0435 \u0432 \u0432\u0438\u0434\u0435 \u043d\u0430\u0434\u0441\u0442\u0440\u043e\u0435\u043a \u043d\u0430\u0434 \u043f\u043e\u0445\u043e\u0436\u0438\u043c\u0438 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430\u043c\u0438 AMD. <\/p>\n

GPU-kodi i aplikacioneve, i kompiliuar n\u00eb formatin PTX (Parallel Thread Execution), p\u00ebrkthehet nga nj\u00eb kompilues t\u00eb ve\u00e7ant\u00eb fillimisht n\u00eb nj\u00eb format nd\u00ebrmjet\u00ebs LLVM IR, mbi baz\u00ebn e t\u00eb cilit gjenerohet kodi binar p\u00ebr GPU-t\u00eb AMD.

\"ZLUDA<\/center>
\n
Burimi: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u041f\u0440\u043e\u0435\u043a\u0442 ZLUDA \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u0438\u043b \u043e\u0442\u043a\u0440\u044b\u0442\u0443\u044e \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044e \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438 CUDA \u0434\u043b\u044f GPU AMD, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0443\u044e \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c \u043d\u0435\u043c\u043e\u0434\u0438\u0444\u0438\u0446\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 CUDA-\u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u044f \u0441 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c\u044e, \u0431\u043b\u0438\u0437\u043a\u043e\u0439 \u043a \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u0439, \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u043c\u044b\u0445 \u0431\u0435\u0437 \u043f\u0440\u043e\u0441\u043b\u043e\u0435\u043a. \u041e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0438\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\u0438\u043d\u0430\u0440\u043d\u0443\u044e \u0441\u043e\u0432\u043c\u0435\u0441\u0442\u0438\u043c\u043e\u0441\u0442\u044c \u0441 \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u043c\u0438 CUDA-\u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u044f\u043c\u0438, \u0441\u043e\u0431\u0440\u0430\u043d\u043d\u044b\u043c\u0438 \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0442\u043e\u0440\u0430 CUDA \u0434\u043b\u044f GPU NVIDIA. \u0420\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u043f\u043e\u0432\u0435\u0440\u0445 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u043c\u043e\u0433\u043e \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0435\u0439 AMD \u0441\u0442\u0435\u043a\u0430 ROCm \u0438 runtime HIP (Heterogeneous-computing Interface for Portability). \u041a\u043e\u0434 […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":113553,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-113552","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-novosti-interneta"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47Seti i veglave ZLUDA \u00ebsht\u00eb publikuar, i cili lejon ekzekutimin e aplikacioneve CUDA n\u00eb GPU-t\u00eb AMD | ProHoster","description":"Projekti ZLUDA ka p\u00ebrgatitur nj\u00eb realizim t\u00eb hapur t\u00eb teknologjis\u00eb CUDA p\u00ebr GPU-t\u00eb AMD, i cili mund\u00ebson ekzekutimin e aplikacioneve CUDA t\u00eb pa modifikuara me performanc\u00eb t\u00eb ngjashme me at\u00eb t\u00eb aplikacioneve q\u00eb ekzekutohen pa nd\u00ebrmjet\u00ebs. Seti i veglave t\u00eb publikuar siguron kompatibilitetin binar me aplikacionet ekzistuese CUDA, t\u00eb nd\u00ebrtuara me ndihm\u00ebn e kompiluesit CUDA p\u00ebr GPU-t\u00eb NVIDIA. Realizimi funksionon sip\u00ebr stakut ROCm t\u00eb zhvilluar nga kompania AMD dhe runtime HIP (Heterogeneous-computing Interface for Portability). Kodi","canonical_url":"https:\/\/prohoster.info\/sq\/blog\/novosti-interneta\/opublikovan-instrumentarij-zluda-pozvolyayushhij-zapuskat-cuda-prilozheniya-na-gpu-amd","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"sq_AL","og:site_name":"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b","og:type":"article","og:title":"\ud83e\udd47\u041e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043d \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 ZLUDA, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0438\u0439 \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0442\u044c CUDA-\u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u044f \u043d\u0430 GPU AMD | ProHoster","og:description":"\u041f\u0440\u043e\u0435\u043a\u0442 ZLUDA \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u0438\u043b \u043e\u0442\u043a\u0440\u044b\u0442\u0443\u044e \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044e \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438 CUDA \u0434\u043b\u044f GPU AMD, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0443\u044e \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c \u043d\u0435\u043c\u043e\u0434\u0438\u0444\u0438\u0446\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 CUDA-\u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u044f \u0441 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c\u044e, \u0431\u043b\u0438\u0437\u043a\u043e\u0439 \u043a 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\u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0442\u043e\u0440\u0430 CUDA \u0434\u043b\u044f GPU NVIDIA. \u0420\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u043f\u043e\u0432\u0435\u0440\u0445 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u043c\u043e\u0433\u043e \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0435\u0439 AMD \u0441\u0442\u0435\u043a\u0430 ROCm \u0438 runtime HIP (Heterogeneous-computing Interface for Portability). 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