{"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\/es\/blog\/news\/tenzornye-i-rt-yadra-zanimayut-ne-tak-mnogo-mesta-na-graficheskih-protsessorah-nvidia-turing","title":{"rendered":"Los n\u00facleos Tensor y RT no ocupan tanto espacio en las GPU NVIDIA Turing","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Desde el anuncio de las primeras tarjetas gr\u00e1ficas GeForce RTX de la serie 20, muchos asumieron que el tama\u00f1o considerable de los procesadores gr\u00e1ficos Turing se deb\u00eda a la presencia de bloques adicionales: n\u00facleos RT y n\u00facleos tensor. Sin embargo, un usuario de Reddit ha analizado im\u00e1genes infrarrojas de los procesadores gr\u00e1ficos Turing TU106 y TU116 y ha concluido que los nuevos bloques de c\u00f3mputo ocupan mucho menos espacio de lo que se pensaba inicialmente.<\/p>\n

\"Los<\/p>\n

Para empezar, recordemos que el procesador gr\u00e1fico Turing TU106 es el chip m\u00e1s peque\u00f1o y compacto de NVIDIA, dise\u00f1ado con n\u00facleos RT especiales para trazado de rayos y n\u00facleos tensor para acelerar funciones de inteligencia artificial. A su vez, el procesador gr\u00e1fico Turing TU116 carece de estos bloques de c\u00f3mputo especiales, lo que llev\u00f3 a decidir compararlos.<\/p>\n

\"Los
\n\"Los<\/p>\n

Las unidades de procesamiento gr\u00e1fico NVIDIA Turing se dividen en bloques TPC, que incluyen un par de multiprocesadores de flujo (Streaming Multiprocessors), que a su vez contienen todos los n\u00facleos de c\u00e1lculo. Y, como se descubri\u00f3, el procesador gr\u00e1fico Turing TU106 tiene un \u00e1rea del bloque TPC que es solo 1,95 mm\u00b2 m\u00e1s grande que la del Turing TU116, es decir, un 22 %. De esta \u00e1rea, 1,25 mm\u00b2 corresponden a los n\u00facleos tensoriales y solo 0,7 mm\u00b2 a los n\u00facleos RT.<\/p>\n

\"Los
\n\"Los<\/p>\n

Por lo tanto, sin los nuevos n\u00facleos tensoriales y RT, el procesador gr\u00e1fico insignia Turing TU102, que se utiliza en la GeForce RTX 2080 Ti, ocupar\u00eda no 754 mm\u00b2, sino 684 mm\u00b2 (36 TPC). A su vez, el Turing TU104, que es la base de la GeForce RTX 2080, podr\u00eda ocupar 498 mm\u00b2 en lugar de 545 mm\u00b2 (24 TPC). Como se puede ver, incluso sin los n\u00facleos tensoriales y RT, los procesadores gr\u00e1ficos Turing m\u00e1s avanzados ser\u00edan chips bastante grandes. Significativamente m\u00e1s grandes que los procesadores gr\u00e1ficos Pascal.<\/p>\n

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

\u00bfPero a qu\u00e9 se deben estas considerables dimensiones? En primer lugar, los procesadores gr\u00e1ficos Turing tienen un mayor volumen de memoria cach\u00e9. Tambi\u00e9n se ha aumentado el tama\u00f1o de los sombreadores, y los chips Turing cuentan con conjuntos de instrucciones m\u00e1s grandes y registros ampliados. Todo esto ha permitido aumentar significativamente no solo el \u00e1rea, sino tambi\u00e9n el rendimiento de los procesadores gr\u00e1ficos Turing. Por ejemplo, la GeForce RTX 2060 basada en TU106 proporciona casi el mismo nivel de rendimiento que la GeForce GTX 1080 basada en GP104. Este \u00faltimo, por cierto, tiene un 25% m\u00e1s de n\u00facleos CUDA, aunque ocupa un \u00e1rea de 314 mm\u00b2 frente a los 410 mm\u00b2 del nuevo TU106. <\/p>\n

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Fuente: 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\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\udd47Los n\u00facleos tensoriales y RT no ocupan tanto espacio en las tarjetas gr\u00e1ficas NVIDIA Turing | ProHoster","description":"Incluso durante el anuncio de las primeras tarjetas gr\u00e1ficas GeForce RTX de la serie 20, muchos consideraron que sus dimensiones bastante grandes se deb\u00edan a la presencia de bloques adicionales: n\u00facleos RT y n\u00facleos 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