{"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\/pt\/blog\/news\/tenzornye-i-rt-yadra-zanimayut-ne-tak-mnogo-mesta-na-graficheskih-protsessorah-nvidia-turing","title":{"rendered":"Os n\u00facleos tensoriais e RT n\u00e3o ocupam muito espa\u00e7o nas placas gr\u00e1ficas NVIDIA Turing.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Durante o an\u00fancio das primeiras placas de v\u00eddeo da s\u00e9rie GeForce RTX 20, muitos acreditaram que as grandes dimens\u00f5es dos processadores gr\u00e1ficos Turing se deviam \u00e0 presen\u00e7a de blocos adicionais: n\u00facleos RT e n\u00facleos tensor. Agora, um usu\u00e1rio do Reddit analisou imagens infravermelhas dos processadores gr\u00e1ficos Turing TU106 e TU116 e concluiu que os novos blocos de computa\u00e7\u00e3o ocupam menos espa\u00e7o do que se pensava inicialmente.<\/p>\n

\"Os<\/p>\n

Para come\u00e7ar, lembremos que o processador gr\u00e1fico Turing TU106 \u00e9 o chip mais compacto da NVIDIA com n\u00facleos RT especiais para rastreamento de raios e n\u00facleos tensor para acelerar fun\u00e7\u00f5es de intelig\u00eancia artificial. Por sua vez, o chip gr\u00e1fico relacionado, o Turing TU116, n\u00e3o possui esses blocos de computa\u00e7\u00e3o especiais, e \u00e9 por isso que foi decidido compar\u00e1-los.<\/p>\n

\"Os
\n\"Os<\/p>\n

Os processadores gr\u00e1ficos NVIDIA Turing s\u00e3o divididos em blocos TPC, que incluem um par de multiprocessadores de fluxo (Streaming Multiprocessors), nos quais j\u00e1 est\u00e3o todos os n\u00facleos de computa\u00e7\u00e3o. E, como se viu, a \u00e1rea do bloco TPC do processador gr\u00e1fico Turing TU106 \u00e9 apenas 1,95 mm\u00b2 maior do que a do Turing TU116, ou seja, 22%. Desses, 1,25 mm\u00b2 s\u00e3o ocupados por n\u00facleos tensorais, e apenas 0,7 mm\u00b2 \u2014 pelos n\u00facleos RT.<\/p>\n

\"Os
\n\"Os<\/p>\n

Isso significa que, sem os novos n\u00facleos tensorais e RT, o processador gr\u00e1fico carro-chefe Turing TU102, que serve de base para o GeForce RTX 2080 Ti, ocuparia n\u00e3o 754 mm\u00b2, mas 684 mm\u00b2 (36 TPC). Por sua vez, o Turing TU104, que \u00e9 a base do GeForce RTX 2080, poderia ocupar 498 mm\u00b2 em vez de 545 mm\u00b2 (24 TPC). Como podemos ver, mesmo sem os n\u00facleos tensorais e RT, os processadores gr\u00e1ficos Turing mais avan\u00e7ados ainda seriam chips bastante grandes. Muito maiores que os processadores gr\u00e1ficos Pascal.<\/p>\n

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

Ent\u00e3o, por que essas dimens\u00f5es consider\u00e1veis? Para come\u00e7ar, os processadores gr\u00e1ficos Turing tiveram um aumento na quantidade de mem\u00f3ria cache. O tamanho dos shaders tamb\u00e9m foi aumentado e os chips Turing possuem conjuntos de instru\u00e7\u00f5es maiores e registradores ampliados. Tudo isso permitiu uma melhora significativa n\u00e3o apenas na \u00e1rea, mas tamb\u00e9m no desempenho dos processadores gr\u00e1ficos Turing. Por exemplo, a GeForce RTX 2060 baseada no TU106 consegue praticamente o mesmo n\u00edvel de desempenho que a GeForce GTX 1080 baseada no GP104. Este \u00faltimo, por sinal, possui 25% mais n\u00facleos CUDA, embora ocupe uma \u00e1rea de 314 mm\u00b2 contra 410 mm\u00b2 do novo 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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[…]<\/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\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\udd47Os n\u00facleos tensor e RT n\u00e3o ocupam muito espa\u00e7o nos processadores gr\u00e1ficos NVIDIA Turing | ProHoster","description":"Ainda durante o an\u00fancio das primeiras placas gr\u00e1ficas GeForce RTX da s\u00e9rie 20, muitos acreditavam que as dimens\u00f5es consider\u00e1veis dos processadores gr\u00e1ficos Turing se deviam \u00e0 presen\u00e7a de m\u00f3dulos adicionais: n\u00facleos RT 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