{"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\/it\/blog\/novosti-interneta\/tenzornye-i-rt-yadra-zanimayut-ne-tak-mnogo-mesta-na-graficheskih-protsessorah-nvidia-turing","title":{"rendered":"I core tensor e i core RT occupano uno spazio ridotto sulle schede grafiche NVIDIA Turing.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Gi\u00e0 durante l'annuncio delle prime schede grafiche GeForce RTX della serie 20, molti hanno ritenuto che le dimensioni piuttosto grandi dei processori grafici Turing fossero dovute alla presenza di blocchi aggiuntivi: i core RT e i core tensor. Tuttavia, un utente di Reddit ha ora analizzato le immagini a infrarossi dei processori grafici Turing TU106 e TU116, concludendo che i nuovi blocchi di calcolo occupano meno spazio di quanto si pensasse inizialmente.<\/p>\n

\"I<\/p>\n

Per cominciare, ricordiamo che il processore grafico Turing TU106 \u00e8 il chip pi\u00f9 piccolo e di base di NVIDIA, dotato di speciali core RT per il ray tracing e core tensor per accelerare le funzionalit\u00e0 di intelligenza artificiale. A sua volta, il gemello Turing TU116 \u00e8 privo di questi blocchi di calcolo speciali e per questo \u00e8 stato deciso di confrontare proprio questi due.<\/p>\n

\"I
\n\"I<\/p>\n

Le GPU NVIDIA Turing \u00e8 suddiviso in blocchi TPC, che comprendono una coppia di multiprocessori a flusso (Streaming Multiprocessors), nei quali sono gi\u00e0 inclusi tutti i core di calcolo. E, come si \u00e8 scoperto, il chip grafico Turing TU106 ha un'area del blocco TPC superiore solo di 1,95 mm\u00b2 rispetto al Turing TU116, ovvero del 22%. Di questa area, 1,25 mm\u00b2 sono destinati ai core tensori, e solo 0,7 mm\u00b2 \u2014 ai core RT.<\/p>\n

\"I
\n\"I<\/p>\n

Quindi, senza i nuovi core tensori e RT, il GPU di punta Turing TU102, alla base del GeForce RTX 2080 Ti, occuperebbe non 754 mm\u00b2, ma 684 mm\u00b2 (36 TPC). A sua volta, il Turing TU104, che \u00e8 alla base del GeForce RTX 2080, potrebbe occupare 498 mm\u00b2 invece di 545 mm\u00b2 (24 TPC). Come si pu\u00f2 notare, anche senza i core tensori e RT, i pi\u00f9 potenti GPU Turing sarebbero ancora chip di dimensioni considerevoli. Significativamente pi\u00f9 grandi rispetto ai GPU Pascal.<\/p>\n

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\n \"I <\/p>\n

Quali sono dunque le ragioni di queste dimensioni non indifferenti? Innanzitutto, i processori grafici Turing hanno visto un aumento della quantit\u00e0 di memoria cache. Inoltre, \u00e8 aumentata la dimensione degli shader e i chip Turing dispongono di set di istruzioni pi\u00f9 ampi e registri ingranditi. Tutto ci\u00f2 ha permesso di incrementare significativamente non solo la superficie, ma anche le prestazioni dei processori grafici Turing. Ad esempio, la GeForce RTX 2060 basata su TU106 offre un livello di prestazioni quasi equivalente a quello della GeForce GTX 1080 basata su GP104. Quest'ultima, per inciso, ha il 25% in pi\u00f9 di nuclei CUDA, nonostante occupi una superficie di 314 mm\u00b2 contro i 410 mm\u00b2 del nuovo TU106. <\/p>\n

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

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