diff --git a/pages/students/2016/lp832ut/timovy_projekt/README.md b/pages/students/2016/lp832ut/timovy_projekt/README.md index f293b722..9e4272f4 100644 --- a/pages/students/2016/lp832ut/timovy_projekt/README.md +++ b/pages/students/2016/lp832ut/timovy_projekt/README.md @@ -10,8 +10,7 @@ Poznáme dve populárne metódy, ktorými sa dá dosiahnuť vysoký výkon pre N Trénovaním modelov hlbokého učenia sa zistilo, že viacero operácií dokáže ťažiť z paralelného spracovania. Medzi tieto operácie patrí napríklad násobenie matíc [3]. Podobne ako grafické animácie, ktoré boli hlavným cieľom pre trh s grafickými kartami, trénovanie modelov hlbokého učenia je významne urýchlený paralelizovaním násobenia matíc. - 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) - + ![Násobenie matíc](./matice.png) _Obr._ _1. Násobenie matíc_ _[3]._ Jednotlivé jadrá grafickej karty sú v porovnaní s jadrami procesora pomalé, ale každé jadro dokáže vypočítať príslušný vektorový komponent. Keby tento výpočet prebiehal na procesore, každý riadok násobenia by sa vykonával sekvenčne. Ak by počet riadkov matice bol _n_, procesor by na výpočet rovnakej úlohy potreboval _n_-krát viacej času. Ak by sme dokázali zredukovať čas potrebný na spracovanie modelu na napríklad na jednu desatinu, v praxi by to znamenalo, že dokážeme vyskúšať desať rôznych prístupov a dosiahnuť oveľa vyššiu presnosť.