{"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\/fr\/blog\/news\/tenzornye-i-rt-yadra-zanimayut-ne-tak-mnogo-mesta-na-graficheskih-protsessorah-nvidia-turing","title":{"rendered":"Les c\u0153urs tensoriels et RT ne prennent pas beaucoup de place sur les processeurs graphiques NVIDIA Turing.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

D\u00e8s l'annonce des premi\u00e8res cartes graphiques GeForce RTX de la s\u00e9rie 20, beaucoup ont consid\u00e9r\u00e9 que les processeurs graphiques Turing \u00e9taient responsables de leurs dimensions cons\u00e9quentes en raison de la pr\u00e9sence de blocs suppl\u00e9mentaires : noyaux RT et noyaux tensoriels. Cependant, un utilisateur de Reddit a r\u00e9cemment analys\u00e9 des images infrarouges des processeurs graphiques Turing TU106 et TU116 et a conclu que les nouveaux blocs de calcul occupent en r\u00e9alit\u00e9 moins d'espace que ce qui \u00e9tait initialement estim\u00e9.<\/p>\n

\"Les<\/p>\n

Pour commencer, rappelons que le processeur graphique Turing TU106 est le plus petit et le plus compact d'NVIDIA, dot\u00e9 de noyaux RT sp\u00e9ciaux pour le ray tracing et de noyaux tensoriels pour acc\u00e9l\u00e9rer les fonctions d'intelligence artificielle. En revanche, le processeur graphique Turing TU116, qui lui est apparent\u00e9, ne poss\u00e8de pas ces blocs de calcul sp\u00e9ciaux et c'est pourquoi leur comparaison a \u00e9t\u00e9 d\u00e9cid\u00e9e.<\/p>\n

\"Les
\n\"Les<\/p>\n

Les processeurs graphiques NVIDIA Turing sont divis\u00e9s en blocs TPC, qui comprennent une paire de multiprocesseurs de flux (Streaming Multiprocessors), incorporant tous les c\u0153urs de calcul. Il s'av\u00e8re que la surface du bloc TPC du processeur graphique Turing TU106 est seulement de 1,95 mm\u00b2 plus grande que celle du Turing TU116, soit une augmentation de 22 %. Sur cette surface, 1,25 mm\u00b2 est consacr\u00e9 aux c\u0153urs tensoriels, et seulement 0,7 mm\u00b2 aux c\u0153urs RT.<\/p>\n

\"Les
\n\"Les<\/p>\n

Ainsi, sans les nouveaux c\u0153urs tensoriels et RT, le processeur graphique phare Turing TU102, qui sert de base au GeForce RTX 2080 Ti, occuperait non pas 754 mm\u00b2, mais 684 mm\u00b2 (36 TPC). De m\u00eame, le Turing TU104, qui est \u00e0 la base du GeForce RTX 2080, pourrait occuper 498 mm\u00b2 au lieu de 545 mm\u00b2 (24 TPC). Comme on peut le voir, m\u00eame sans les c\u0153urs tensoriels et RT, les processeurs graphiques Turing de gamme sup\u00e9rieure seraient des puces de taille consid\u00e9rable, beaucoup plus grandes que celles des processeurs graphiques Pascal.<\/p>\n

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\n \"Les <\/p>\n

Alors, pourquoi ces dimensions relativement importantes ? Tout d'abord, les processeurs graphiques Turing ont vu la quantit\u00e9 de m\u00e9moire cache augmenter. La taille des shaders a \u00e9galement \u00e9t\u00e9 augment\u00e9e, et les puces Turing disposent de jeux d'instructions plus vastes ainsi que de registres \u00e9largis. Tout cela a permis d'am\u00e9liorer de fa\u00e7on significative \u00e0 la fois la surface et les performances des processeurs graphiques Turing. Par exemple, la GeForce RTX 2060 bas\u00e9e sur TU106 offre presque le m\u00eame niveau de performance que la GeForce GTX 1080 bas\u00e9e sur GP104. Ce dernier, d'ailleurs, poss\u00e8de 25 % de c\u0153urs CUDA en plus, bien qu'il occupe une superficie de 314 mm2 contre 410 mm2 pour le nouveau TU106. <\/p>\n

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Source : 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\udd47Les c\u0153urs Tensor et RT ne prennent pas beaucoup de place sur les processeurs graphiques NVIDIA Turing | ProHoster","description":"D\u00e8s l'annonce des premi\u00e8res cartes graphiques GeForce RTX de la s\u00e9rie 20, beaucoup ont pens\u00e9 que leurs dimensions imposantes \u00e9taient dues \u00e0 la pr\u00e9sence de blocs suppl\u00e9mentaires : c\u0153urs RT et c\u0153urs 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