{"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\/news\/tenzornye-i-rt-yadra-zanimayut-ne-tak-mnogo-mesta-na-graficheskih-protsessorah-nvidia-turing","title":{"rendered":"I core tensor e RT occupano poco spazio 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 di serie 20, molti hanno ritenuto che le dimensioni piuttosto grandi dei processori grafici Turing fossero dovute alla presenza di ulteriori unit\u00e0: nuclei RT e nuclei tensoriali. Ora, uno degli utenti di Reddit ha analizzato le immagini infrarosse dei processori grafici Turing TU106 e TU116 e ha concluso 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 junior di NVIDIA con i suoi nuclei RT speciali per il ray tracing e i nuclei tensoriali per accelerare le funzioni di intelligenza artificiale. A sua volta, il processore grafico Turing TU116, a lui collegato, \u00e8 privo di questi blocchi di calcolo speciali, ed \u00e8 proprio per questo che si \u00e8 deciso di confrontarli.<\/p>\n

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

Le GPU NVIDIA Turing sono suddivisi in blocchi TPC, che includono un paio di multiprocessori di flusso (Streaming Multiprocessors), all'interno dei quali si trovano tutti i core di calcolo. E, come si \u00e8 scoperto, il GPU Turing TU106 ha un'area del blocco TPC che \u00e8 solo di 1,95 mm\u00b2 pi\u00f9 grande rispetto a quella del Turing TU116, cio\u00e8 del 22%. Di quest'area, 1,25 mm\u00b2 sono dedicati ai core tensoriali, mentre solo 0,7 mm\u00b2 sono riservati ai core RT.<\/p>\n

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

Di conseguenza, senza i nuovi core tensoriali e RT, il flagship GPU Turing TU102, alla base della GeForce RTX 2080 Ti, occuperebbe non 754 mm\u00b2, ma 684 mm\u00b2 (36 TPC). Allo stesso modo, il Turing TU104, che \u00e8 alla base della GeForce RTX 2080, potrebbe occupare 498 mm\u00b2 invece di 545 mm\u00b2 (24 TPC). Come si pu\u00f2 vedere, anche senza i core tensoriali e RT, i GPU Turing di fascia alta sarebbero stati chip piuttosto grandi, significativamente pi\u00f9 grandi dei GPU Pascal.<\/p>\n

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

Allora, a cosa sono dovute queste dimensioni piuttosto considerevoli? Per cominciare, il volume della memoria cache \u00e8 stato aumentato nei processori grafici Turing. Inoltre \u00e8 stata aumentata la dimensione degli shader, e i chip Turing possiedono set di istruzioni pi\u00f9 ampi e registri ingranditi. Tutto questo ha consentito di aumentare notevolmente non solo l'area, ma anche le prestazioni dei processori grafici Turing. Per esempio, la stessa GeForce RTX 2060 basata su TU106 offre prestazioni quasi pari a quelle della GeForce GTX 1080 basata su GP104. Quest'ultima, tra l'altro, ha un 25% in pi\u00f9 di nuclei CUDA, anche se occupa un'area di 314 mm\u00b2 contro 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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