{"id":31358,"date":"2019-10-31T21:40:51","date_gmt":"2019-10-31T18:40:51","guid":{"rendered":"https:\/\/prohoster.info\/blog\/tenzornye-i-rt-yadra-zanimayut-ne-tak-mnogo-mesta-na-graficheskih-protsessorah-nvidia-turing\/"},"modified":"2019-10-31T21:40:51","modified_gmt":"2019-10-31T18:40:51","slug":"tenzornye-i-rt-yadra-zanimayut-ne-tak-mnogo-mesta-na-graficheskih-protsessorah-nvidia-turing","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/news\/tenzornye-i-rt-yadra-zanimayut-ne-tak-mnogo-mesta-na-graficheskih-protsessorah-nvidia-turing","title":{"rendered":"Tensor- ja RT- tuumad ei v\u00f5ta NVIDIA Turingi graafikaprotsessoritel liiga palju ruumi","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Juba juba, et RTX 20 seeria esimeste graafikakaartide esitlemise ajal arvasid paljud, et Turing\u2019i graafikaprotsessorid on oma \u00fcsna suurtel m\u00f5\u00f5tmetel kohustatud olema t\u00e4iendavate plokkide: RT-tuuma ja tensori tuuma olemasolule. N\u00fc\u00fcd on \u00fcks Redditi kasutaja anal\u00fc\u00fcsinud Turing TU106 ja TU116 graafikaprotsessorite infrapuna pilte ning leidnud, et uued arvutusblokkid ei tarvitse nii palju ruumi kui alguses arvati.<\/p>\n

\"Tensor-<\/p>\n

K\u00e4esoleku kiire meenutamine on, et Turing TU106 graafikaprotsessor on NVIDIA k\u00f5ige v\u00e4iksem ja kompaktsem kiip, millel on spetsiaalsed RT-tuuma kiired kiirte j\u00e4lgimiseks ja tensor tuumad, et kiirendada kunstliku intelligentsuse \u00fclesandeid. Samas, tema sugulane, Turing TU116, on neist spetsiaalsetest arvutusplokkidest puudulik ja just seet\u00f5ttu otsustati neid v\u00f5rrelda.<\/p>\n

\"Tensor-
\n\"Tensor-<\/p>\n

NVIDIA Turing graafikaprotsessorid jagunevad TPC plokkideks, mis sisaldavad paari voolu multiprotsessori (Streaming Multiprocessors), kuhu kuuluvad k\u00f5ik arvutuskerad. N\u00e4ib, et Turing TU106 graafikaprotsessori TPC ploki pindala on vaid 1,95 mm\u00b2 suurem kui Turing TU116, mis t\u00e4hendab 22%. Sellest pindalast 1,25 mm\u00b2 on tENSORkeradele ja vaid 0,7 mm\u00b2 RT-keradele.<\/p>\n

\"Tensor-
\n\"Tensor-<\/p>\n

Seega, ilma uute tENSOR- ja RT-keradeta oleks lipulaeva graafikaprotsessor Turing TU102, mis on GeForce RTX 2080 Ti aluseks, h\u00f5ivanud mitte 754 mm\u00b2, vaid 684 mm\u00b2 (36 TPC). Turing TU104, mis on GeForce RTX 2080 aluseks, oleks v\u00f5inud h\u00f5ivata 498 mm\u00b2 asemel 545 mm\u00b2 (24 TPC). Nagu n\u00e4ha, isegi ilma tENSOR- ja RT-keradeta oleksid vanemad Turing graafikaprotsessorid olnud \u00fcsna suured kiibid. M\u00e4rgatavalt suuremad kui Pascal graafikaprotsessorid.<\/p>\n

<\/center>
\n \"Tensor- <\/p>\n

Miks on need m\u00f5\u00f5dud ikkagi nii suured? Esiteks, Turing\u2019i graafikaprotsessorite vahem\u00e4lu maht on suurenenud. Samuti on suurenenud shaderite suurus ning Turing kiibid k\u00e4sitlevad suuremaid k\u00e4skluste komplekte ja suurendatud registrite arvu. K\u00f5ik see aitas oluliselt suurendada mitte ainult pindala, vaid ka Turing\u2019i graafikaprotsessorite j\u00f5udlust. N\u00e4iteks GeForce RTX 2060, millel on TU106 baasil, pakub peaaegu sama j\u00f5udlustaseme, nagu GeForce GTX 1080 GP104-l. Viimasel on, muide, 25% rohkem CUDA tuumasid, kuigi see h\u00f5ivab 314 mm2, v\u00f5rreldes 410 mm2 uue TU106-ga. <\/p>\n

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Allikas: 3dnews.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u0415\u0449\u0451 \u0432\u043e \u0432\u0440\u0435\u043c\u044f \u0430\u043d\u043e\u043d\u0441\u0430 \u043f\u0435\u0440\u0432\u044b\u0445 \u0432\u0438\u0434\u0435\u043e\u043a\u0430\u0440\u0442 GeForce RTX 20-\u0439 \u0441\u0435\u0440\u0438\u0438 \u043c\u043d\u043e\u0433\u0438\u0435 \u043f\u043e\u0441\u0447\u0438\u0442\u0430\u043b\u0438, \u0447\u0442\u043e \u0441\u0432\u043e\u0438\u043c\u0438 \u0441\u043e\u0432\u0441\u0435\u043c \u043d\u0435 \u043c\u0430\u043b\u0435\u043d\u044c\u043a\u0438\u043c \u0433\u0430\u0431\u0430\u0440\u0438\u0442\u0430\u043c\u0438 \u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u044b Turing \u043e\u0431\u044f\u0437\u0430\u043d\u044b \u043d\u0430\u043b\u0438\u0447\u0438\u044e \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u0431\u043b\u043e\u043a\u043e\u0432: RT-\u044f\u0434\u0435\u0440 \u0438 \u0442\u0435\u043d\u0437\u043e\u0440\u043d\u044b\u0445 \u044f\u0434\u0435\u0440. \u0422\u0435\u043f\u0435\u0440\u044c \u0436\u0435 \u043e\u0434\u0438\u043d \u0438\u0437 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u0435\u0439 Reddit \u043f\u0440\u043e\u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043b \u0438\u043d\u0444\u0440\u0430\u043a\u0440\u0430\u0441\u043d\u044b\u0435 \u0441\u043d\u0438\u043c\u043a\u0438 \u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u043e\u0432 Turing TU106 \u0438 TU116 \u0438 \u0437\u0430\u043a\u043b\u044e\u0447\u0438\u043b, \u0447\u0442\u043e \u043d\u043e\u0432\u044b\u0435 \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0431\u043b\u043e\u043a\u0438 \u0437\u0430\u043d\u0438\u043c\u0430\u044e\u0442 \u043d\u0435 \u0442\u0430\u043a \u043c\u043d\u043e\u0433\u043e \u043c\u0435\u0441\u0442\u0430, […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":23324,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-31358","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"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\udd47Tensorkerad ja RT-kerad ei vaja NVIDIA Turingi graafilistes protsessorites palju ruumi | ProHoster","description":"Juba esialgse GeForce RTX 20-seeria videokaartide esitlusest arvati, et Turingi graafiliste protsessorite suured m\u00f5\u00f5tmed on tingitud t\u00e4iendavatest plokkidest: RT-kerad ja 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