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AI prefetching i budućnost seckanja","what-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u003Cp>Igra retko zna tačnu budućnost, ali zna mnogo više o narednih nekoliko frejmova nego aplikacija opšte namene. Zna vektor kamere, brzinu igrača, vidljive ćelije, obližnje portale, zavisnosti resursa, stanje animacije i često koji će se šejderi ili efekti verovatno pokrenuti sledeći. To čini gejming neobično zanimljivim ciljem za prediktivno upravljanje memorijom.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Direktan odgovor\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>AI-potpomognuto prefetčovanje bi verovatno moglo da smanji neke zastoje povezane sa memorijom, ali ne bi bilo univerzalno dugme protiv seckanja.\u003C\u002Fstrong> Istraživanja već pokazuju da duboko-učeći prefetčeri poboljšavaju predviđanje stranica u objedinjenoj memoriji CPU-GPU. Gejming engine bi mogao da doda nešto što ti benchmarkovi nemaju: eksplicitno stanje sveta i igrača. Hipoteza Figure Rocks je da se oboje kombinuje.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Hipoteza Figure Rocks, ne najavljena funkcija\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Termin \u003Cstrong>Prediktivna gejming memorija\u003C\u002Fstrong> u ovom članku opisuje predloženu arhitekturu: determinističko znanje engine-a + naučeno predviđanje pristupa + prefetčovanje + politika rezidentnosti. To nije naziv proizvoda kompanija NVIDIA, Microsoft, Epic ili AMD.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Sadržaj\">\u003Cstrong class=\"editorjs-toc__title\">Sadržaj\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-5\" class=\"editorjs-toc__link\">Prvo razdvojite memorijske zastoje od drugih seckanja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-8\" class=\"editorjs-toc__link\">Prefetčovanje već postoji; novo pitanje je kako predviđa\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-11\" class=\"editorjs-toc__link\">Već postoje istraživački dokazi za naučeno GPU prefetčovanje\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-16\" class=\"editorjs-toc__link\">Igre imaju dodatne informacije koje generički prefetčer nema\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-19\" class=\"editorjs-toc__link\">Motor može da kombinuje poznatu budućnost sa predviđenom budućnošću\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-21\" class=\"editorjs-toc__link\">Jednostavan primer: trčanje ka vratima\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-23\" class=\"editorjs-toc__link\">Zašto bi ovo moglo biti važnije kako neuronsko renderovanje raste\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-26\" class=\"editorjs-toc__link\">Načini otkaza su jednako važni kao i prilika\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-29\" class=\"editorjs-toc__link\">Kako biste to testirali u stvarnom motoru\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-31\" class=\"editorjs-toc__link\">Gde se ova hipoteza zaustavlja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">Lagana veza sa opštom AI arhitekturom\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-36\" class=\"editorjs-toc__link\">Primarni izvor\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Prvo razdvojite memorijske zastoje od drugih seckanja\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Izvori seckanja koji mogu izgledati slično\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Memorijska\u002Fdata putanja\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Druga putanja\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Resurs nedostaje u brzoj memoriji\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Moguće\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Skladištenje ili dekompresija takođe mogu biti uključeni\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Šejder nije spreman\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Može uključivati keševe\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Kompilacija\u002Fkreiranje pipeline-a može biti pravi uzrok\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Skok CPU simulacije\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Ponašanje keša\u002Fmemorije može doprineti\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Logika igre ili draw-call rad mogu dominirati\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Onlajn zastoj\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Obično nije primarni uzrok\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Server ili tajming paketa mogu dominirati\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Pritisak izbacivanja\u002Frezidentnosti\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Direktno relevantno\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Može se pogrešno dijagnostikovati samo na osnovu merača alokacije\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">Zašto se PC igre seckaju pri kompilaciji šejdera — i kako napredna isporuka šejdera to menja\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Seckanje zbog šejdera je drugačiji problem od predviđanja memorijskih stranica i ne treba ga spojiti u jednu dijagnozu.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Razdvojite seckanje šejdera od memorijskih zastoja →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-8\">Prefetčovanje već postoji; novo pitanje je kako predviđa\u003C\u002Fh2>\n\u003Cp>Tradicionalno prefetčovanje pokušava da premesti podatke pre nego što procesor eksplicitno zatraži. Hardverski prefetčeri iskorišćavaju obrasce pristupa. Engine-i strimuju ćelije sveta pre nego što igrač stigne do njih. Operativni sistemi i runtime-ovi drže verovatno potrebne stranice toplim. Težak deo je lažno predviđanje: prefetčujte premalo i još uvek imate zastoj; prefetčujte previše i trošite propusni opseg i izbacujete korisne podatke.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Osnovni kompromis prefetčovanja\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Posmatraj\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Prikupljaj nedavne pristupe, stanje sveta i kontekst radnog opterećenja.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Predvidi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Proceni koje će stranice\u002Fresursi\u002Fšejderi biti potrebni sledeći.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Premesti\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Prenesi ili učini te resurse rezidentnim pre potražnje.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Iskoristi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">GPU troši resurs bez čekanja ako je predviđanje bilo tačno.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Ispravi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Izbaci pogrešna predviđanja i ažuriraj model\u002Fpolitiku.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-11\">Već postoje istraživački dokazi za naučeno GPU prefetčovanje\u003C\u002Fh2>\n\u003Cp>Objavljena studija o prefetčovanju zasnovanom na dubokom učenju za CPU-GPU objedinjenu virtuelnu memoriju koristila je Transformer za modelovanje globalnog konteksta pristupa memoriji, a zatim izvela lakši prediktor. Na 11 benchmarkova intenzivnih po memoriji, rad je prijavio poboljšanje performansi od 10,89% u odnosu na upoređeni okvir, stopu pogodaka stranica u memoriji uređaja od 89,02% naspram 76,10%, i 11,05% manji saobraćaj na CPU-GPU interkonekciji.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Rezultat istraživanja\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Prijavljena vrednost\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Šta podržava\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Šta NE dokazuje\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Naučene UVM performanse\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">+10,89%\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Naučeno predviđanje pristupa može nadmašiti prethodni UVM prefetch baseline\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Komercijalna igra dobija 10,89% FPS\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Stopa pogodaka UVM stranica\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">89,02% vs 76,10%\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Predviđanje može materijalno promeniti gde se pronalaze traženi podaci\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Svaka tekstura\u002Fstranica u igri je jednako predvidiva\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Saobraćaj na CPU–GPU interkonekciji\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">−11,05%\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Bolji smeštaj može smanjiti reaktivno premeštanje\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">PCIe saobraćaj u igrama uvek opada\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Efikasnost AAAI attention kernel-a\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">2,15×\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Prefetch može sakriti skup pristup memoriji iza korisnog računanja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Gejming kernel-i dobijaju 2,15×\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">AAAI end-to-end propusnost\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Do 1,97×\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Skrivanje latencije može se propagirati dalje od jednog kernel-a\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Multiplikator frame-rate-a igre od 1,97×\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Ne kopirajte ove brojeve na igre\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Ti rezultati dolaze iz benchmark skupa tog rada, \u003Cstrong>ne iz komercijalnih gejming engine-a\u003C\u002Fstrong>. Dokazuju da naučeno predviđanje pristupa memoriji može funkcionisati u GPU UVM radnim opterećenjima. Ne dokazuju dobitak od 10,89% FPS ili specifično smanjenje seckanja u igri.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cp>Studija o naučenom UVM-u je korisna jer predviđa koji je pristup stranici verovatan na osnovu ponašanja memorije. Rad o AAAI KV-kešu iz 2026. je koristan iz drugog razloga: pokazuje da raspoređivač može iskoristiti inače neiskorišćene prozore propusnog opsega da odluči kada da premesti podatke u brži keš pre potražnje. Zajedno podržavaju dva odvojena mehanizma u hipotezi Figure Rocks — predviđanje i tajming — dok nijedan rad nije gejming benchmark.\u003C\u002Fp>\n\u003Ch2 id=\"section-16\">Igre imaju dodatne informacije koje generički prefetčer nema\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Petlja prediktivne gejming memorije\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. Pročitaj determinističko stanje\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Smer kamere, pozicija, brzina, graf nivoa, vidljivost, animacija, stanje questa ili susreta.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. Pročitaj istoriju pristupa\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koje su stranice tekstura, mreže, šejderi, klasteri geometrije i podaci modela nedavno korišćeni.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. Predvidi radni skup bliske budućnosti\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Proceni verovatno potrebne resurse nekoliko frejmova ili sekundi unapred u zavisnosti od cene.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. Dodeli pouzdanost i cenu\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Resurs od 4 MB sa 95% verovatnoće je drugačija odluka od regiona od 2 GB sa 20% verovatnoće.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. Prefetčuj u okviru budžeta propusnog opsega\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Ne dozvoli da samo predviđanje zasiti putanju potrebnu trenutnom renderovanju.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">6. Meri promašaje i zagađenje\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Prati izbegnute zastoje, pogrešna predviđanja i izbačene korisne podatke.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">7\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">7. Prilagodi se\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Ažuriraj prediktor ili politiku engine-a za stvarnu scenu i ponašanje igrača.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Zanimljivi hibrid\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Igra ne mora da bira između ručno pisanog strimovanja i AI. \u003Cstrong>Najjači dizajn može biti hibrid:\u003C\u002Fstrong> deterministička pravila garantuju kritične resurse, dok naučeni prediktor rangira opcioni ili bliski budući rad, bira dubinu prefetčovanja i troši samo bezbednu rezervu propusnog opsega. Ako je pouzdanost niska, sistem se vraća na autorizovana pravila.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-19\">Motor može da kombinuje poznatu budućnost sa predviđenom budućnošću\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Determinističko znanje naspram naučenog predviđanja\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Motor već zna\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Prediktor može da proceni\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Stanje kamere \u002F portala\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Trenutni prikaz, frustum, graf soba, vidljivi portal\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">U kom smeru će se igrač verovatno sledeće okrenuti\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Kretanje igrača\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Brzina, unos, navigaciona ograničenja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Verovatna putanja kretanja nekoliko trenutaka unapred\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Zavisnosti resursa\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Zavisnosti za poznatu ćeliju nivoa ili objekat\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Koji opcioni susedni resursi će postati aktivni\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">LOD stanje\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Trenutna udaljenost i pragovi\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Koje LOD tranzicije su verovatno uskoro\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Graf materijala\u002Fšejdera\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Šejderi potrebni za poznati vidljivi sadržaj\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Koje varijante će verovatno biti zatražene sledeće\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Snaga jezgra\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Poznaje čvrsta pravila nivoa, zavisnosti i garantovane tranzicije\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Rangira dvosmislen budući pristup na osnovu ponašanja i konteksta\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Način otkaza\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Može biti konzervativan i učitavati široke skupove\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Može pogrešiti i zagaditi keš ili memoriju\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Najbolja upotreba\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Kritični resursi i garantovane tranzicije\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Opciono prioritizovanje unutar ograničenog budžeta propusnog opsega\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Bezbednosna politika\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Lako je razumeti i testirati\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Zahteva pragove pouzdanosti, telemetriju i deterministički fallback\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-21\">Jednostavan primer: trčanje ka vratima\u003C\u002Fh2>\n\u003Cp>Zamislite igrača koji trči niz hodnik ka vratima koja se otvaraju u gustu pijacu. Konvencionalni sistem strimovanja već zna sledeću zonu i može početi da je učitava. Prediktivna memorija dodaje drugi sloj: na osnovu smera kamere, kretanja, prethodnih izbora igrača i trenutnog stanja misije, može rangirati koji podskup tekstura, animacionih podataka NPC-ova, šejdera i geometrije će najverovatnije biti potreban prvi.\u003C\u002Fp>\n\u003Ch2 id=\"section-23\">Zašto bi ovo moglo biti važnije kako neuronsko renderovanje raste\u003C\u002Fh2>\n\u003Cp>Neuronska grafika dodaje nove klase podataka: težine modela, latentne ili kompresovane reprezentacije, istorijske bafere i međutenzore. Ako se ti resursi takmiče sa teksturama i geometrijom, rezidentnost postaje komplikovanija. Prediktivno zakazivanje moglo bi da odluči ne samo koji resurs premestiti, već i koju reprezentaciju zadržati: sirove podatke, kompresovanu neuronsku reprezentaciju ili rekonstruktivnu formu.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">DLSS 5 nije samo upscaling: šta generativno neuronsko renderovanje zapravo menja\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Neuronsko renderovanje menja koje podatke frejm zahteva i kada ih zahteva.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte analizu neuronskog renderovanja →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-26\">Načini otkaza su jednako važni kao i prilika\u003C\u002Fh2>\n\u003Cp>Prefetch nije besplatan. Pogrešna predikcija može dohvatiti resurse koje igrač nikada ne vidi, izbaciti korisne stranice i takmičiti se sa saobraćajem kritičnim za frejm. Ispravan cilj stoga nije maksimalni prefetch; to je maksimalna korisna stopa pogodaka unutar ograničenog budžeta propusnog opsega i rezidentnosti.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Način otkaza\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Šta se dešava\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Ideja za ublažavanje\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Loša predikcija\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pogrešni podaci se prefetch-uju\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pragovi pouzdanosti; rezervisati budžet za determinističku potražnju\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zagađenje keša\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Korisni trenutni podaci se izbacuju\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Odvojiti predviđene i zaštićene klase rezidentnosti\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Nadmetanje za propusni opseg\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Prefetch krade propusni opseg od trenutnog frejma\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zakazivati samo u prozorima sa slobodnim kapacitetom; sprovesti budžet po frejmu\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Promena ponašanja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Igrač se okreće ili brzo putuje\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Jeftino otkazivanje i brzi fallback\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Trošak modela\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Prediktor košta više nego što štedi\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Mali modeli, offline obuka ili lagani prediktori izvedeni iz pravila\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zastoj koji nije memorijski\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Problem ostaje\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Držati dijagnostiku odvojeno: šejder, CPU, mreža i putanje skladištenja zahtevaju sopstvene testove\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-29\">Kako biste to testirali u stvarnom motoru\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Evaluacija prediktivne memorije\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">A. Uspostaviti osnovu\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Zabeležiti skokove vremena frejma, page fault-ove\u002Fmigracije, promašaje rezidentnosti i obim prenosa podataka bez predikcije.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">B. Dodati samo deterministički prefetch\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Izmeriti šta konvencionalno znanje motora već rešava.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">C. Dodati naučeno rangiranje\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koristiti isti budžet memorije i propusnog opsega kako bi poređenje bilo fer.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">D. Izmeriti stopu pogodaka i zagađenje\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Pratiti korisne prefetch-ovane bajtove naspram izgubljenih bajtova i izbacivanja.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">E. Izmeriti distribuciju vremena frejma\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Prosečan FPS nije dovoljan; ispitati repnu latenciju i učestalost zastoja.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">F. Testirati promene ponašanja pod stresom\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Brzi zaokreti, teleport, nepredvidivost u multiplayer-u i proceduralne scene.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">7\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">G. Uporediti trošak\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Vreme prediktora na CPU\u002FGPU + memorijski overhead mora biti niže od zastoja koje uklanja.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-31\">Gde se ova hipoteza zaustavlja\u003C\u002Fh2>\n\u003Cp>Prediktivna memorija ne može da učini premali memorijski pool beskonačno velikim. Ne može da stvori propusni opseg. Ne može da popravi zastoj pri kompilaciji šejdera pogađanjem sledeće teksture. I postaje manje korisna kada je budućnost igrača namerno nepredvidiva ili kada motor već ima skoro savršene determinističke informacije o strimovanju.\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">Lagana veza sa opštom AI arhitekturom\u003C\u002Fh2>\n\u003Cp>Dublji obrazac je sličan retrieval sistemima izvan gejminga: sistem ne treba da dohvata sve, i ne treba da čeka do poslednjeg trenutka da otkrije da kontekst nedostaje. Potreban mu je trigger, skup kandidata, politika rangiranja i granica validnosti. Gejming jednostavno nameće mnogo stroži budžet latencije.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">Šta je RAG? Najjednostavnije objašnjenje kako funkcioniše\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Opšte objašnjenje retrieval-a, konteksta i zašto sistemi dohvataju informacije umesto da sve odjednom smeste u model.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte RAG objašnjenje na stajic.de →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-36\">Primarni izvor\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Deep learning based data prefetching in CPU-GPU Unified Virtual Memory\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Research paper reporting learned UVM page-prefetching results across 11 memory-intensive benchmarks.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">AAAI 2026 — Asynchronous KV Cache Prefetching\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Published 2026 evidence for proactively using idle memory bandwidth and L2 cache prefetch.\u003C\u002Fp>\u003C\u002Fa>\n\u003Csection class=\"editorjs-faq my-6 rounded-xl border border-gray-200 p-5 dark:border-gray-700\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Predictive gaming memory\u003C\u002Fh3>\u003Cdiv id=\"faq1\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Is AI prefetching already a standard game-engine feature?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Not in the specific Figure Rocks architecture proposed here. Games already use many forms of prefetching and predictive streaming; the hypothesis is to combine deterministic game state with learned GPU-memory prediction.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq2\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Would this increase RAM or VRAM speed?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. It aims to improve when and what data is moved so the processor waits less often.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq3\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Could it eliminate stutter?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. It can only address stalls caused by data availability\u002Fresidency when prediction is accurate enough.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq4\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Why might games be a good prediction target?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Because engines expose structured information about camera, level topology, assets, animation and likely visibility that a generic application does not.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq5\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">What metric matters most?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Not one metric. Useful evaluation needs page\u002Fresource hit rate, bandwidth overhead, cache pollution and frame-time tail latency.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Csection class=\"editorjs-glossary my-6 rounded-xl border border-gray-200 dark:border-gray-700 p-5\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Predictive memory terms\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"prefetch\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Prefetch\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Moving or making data available before explicit demand.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"residency\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Residency\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Keeping the resource in memory close enough to the processor that it can be used without an expensive transfer.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"cache-pollution\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Cache pollution\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Replacing useful data with prefetched data that turns out not to be needed.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"predictive-gaming-memory\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Predictive Gaming Memory\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Figure Rocks hypothesis combining deterministic engine state, learned prediction, prefetch and measured residency policy.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>",{"time":538,"blocks":539,"version":1034},1792339170153,[540,546,554,561,568,574,619,628,633,638,661,666,671,707,714,719,724,751,758,763,830,835,840,845,850,858,863,868,900,905,932,937,942,947,952,960,965,975,984,1011],{"id":541,"data":542,"type":544,"tunes":545},"p-0075",{"text":543},"Igra retko zna tačnu budućnost, ali zna mnogo više o narednih nekoliko frejmova nego aplikacija opšte namene. Zna vektor kamere, brzinu igrača, vidljive ćelije, obližnje portale, zavisnosti resursa, stanje animacije i često koji će se šejderi ili efekti verovatno pokrenuti sledeći. To čini gejming neobično zanimljivim ciljem za prediktivno upravljanje memorijom.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"c-0076",{"body":549,"title":550,"variant":551},"\u003Cstrong>AI-potpomognuto prefetčovanje bi verovatno moglo da smanji neke zastoje povezane sa memorijom, ali ne bi bilo univerzalno dugme protiv seckanja.\u003C\u002Fstrong> Istraživanja već pokazuju da duboko-učeći prefetčeri poboljšavaju predviđanje stranica u objedinjenoj memoriji CPU-GPU. Gejming engine bi mogao da doda nešto što ti benchmarkovi nemaju: eksplicitno stanje sveta i igrača. Hipoteza Figure Rocks je da se oboje kombinuje.","Direktan odgovor","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"c-0077",{"body":557,"title":558,"variant":559},"Termin \u003Cstrong>Prediktivna gejming memorija\u003C\u002Fstrong> u ovom članku opisuje predloženu arhitekturu: determinističko znanje engine-a + naučeno predviđanje pristupa + prefetčovanje + politika rezidentnosti. To nije naziv proizvoda kompanija NVIDIA, Microsoft, Epic ili AMD.","Hipoteza Figure Rocks, ne najavljena funkcija","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc-0078",{"title":564,"maxLevel":565,"minLevel":47},"Sadržaj",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-0079",{"text":571,"level":47},"Prvo razdvojite memorijske zastoje od drugih seckanja","header",{},{"id":575,"data":576,"type":617,"tunes":618},"cmp-0080",{"rows":577,"title":608,"layout":609,"columns":610},[578,584,590,596,602],{"id":579,"label":580,"values":581},"asset","Resurs nedostaje u brzoj memoriji",{"other":582,"memory":583},"Skladištenje ili dekompresija takođe mogu biti uključeni","Moguće",{"id":585,"label":586,"values":587},"shader","Šejder nije spreman",{"other":588,"memory":589},"Kompilacija\u002Fkreiranje pipeline-a može biti pravi uzrok","Može uključivati keševe",{"id":591,"label":592,"values":593},"cpu","Skok CPU simulacije",{"other":594,"memory":595},"Logika igre ili draw-call rad mogu dominirati","Ponašanje keša\u002Fmemorije može doprineti",{"id":597,"label":598,"values":599},"network","Onlajn zastoj",{"other":600,"memory":601},"Server ili tajming paketa mogu dominirati","Obično nije primarni uzrok",{"id":603,"label":604,"values":605},"vram","Pritisak izbacivanja\u002Frezidentnosti",{"other":606,"memory":607},"Može se pogrešno dijagnostikovati samo na osnovu merača alokacije","Direktno relevantno","Izvori seckanja koji mogu izgledati slično","table",[611,614],{"id":612,"label":613},"memory","Memorijska\u002Fdata putanja",{"id":615,"label":616},"other","Druga putanja","comparison",{},{"id":620,"data":621,"type":626,"tunes":627},"ref-0081",{"url":622,"title":623,"excerpt":624,"ctaLabel":625},"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it","Zašto se PC igre seckaju pri kompilaciji šejdera — i kako napredna isporuka šejdera to menja","Seckanje zbog šejdera je drugačiji problem od predviđanja memorijskih stranica i ne treba ga spojiti u jednu dijagnozu.","Razdvojite seckanje šejdera od memorijskih zastoja","referralArticle",{},{"id":629,"data":630,"type":572,"tunes":632},"h-0082",{"text":631,"level":47},"Prefetčovanje već postoji; novo pitanje je kako predviđa",{},{"id":634,"data":635,"type":544,"tunes":637},"p-0083",{"text":636},"Tradicionalno prefetčovanje pokušava da premesti podatke pre nego što procesor eksplicitno zatraži. Hardverski prefetčeri iskorišćavaju obrasce pristupa. Engine-i strimuju ćelije sveta pre nego što igrač stigne do njih. Operativni sistemi i runtime-ovi drže verovatno potrebne stranice toplim. Težak deo je lažno predviđanje: prefetčujte premalo i još uvek imate zastoj; prefetčujte previše i trošite propusni opseg i izbacujete korisne podatke.",{},{"id":639,"data":640,"type":659,"tunes":660},"flow-0084",{"steps":641,"title":657,"orientation":658},[642,645,648,651,654],{"label":643,"description":644},"Posmatraj","Prikupljaj nedavne pristupe, stanje sveta i kontekst radnog opterećenja.",{"label":646,"description":647},"Predvidi","Proceni koje će stranice\u002Fresursi\u002Fšejderi biti potrebni sledeći.",{"label":649,"description":650},"Premesti","Prenesi ili učini te resurse rezidentnim pre potražnje.",{"label":652,"description":653},"Iskoristi","GPU troši resurs bez čekanja ako je predviđanje bilo tačno.",{"label":655,"description":656},"Ispravi","Izbaci pogrešna predviđanja i ažuriraj model\u002Fpolitiku.","Osnovni kompromis prefetčovanja","auto","processFlow",{},{"id":662,"data":663,"type":572,"tunes":665},"h-0085",{"text":664,"level":47},"Već postoje istraživački dokazi za naučeno GPU prefetčovanje",{},{"id":667,"data":668,"type":544,"tunes":670},"p-0086",{"text":669},"Objavljena studija o prefetčovanju zasnovanom na dubokom učenju za CPU-GPU objedinjenu virtuelnu memoriju koristila je Transformer za modelovanje globalnog konteksta pristupa memoriji, a zatim izvela lakši prediktor. Na 11 benchmarkova intenzivnih po memoriji, rad je prijavio poboljšanje performansi od 10,89% u odnosu na upoređeni okvir, stopu pogodaka stranica u memoriji uređaja od 89,02% naspram 76,10%, i 11,05% manji saobraćaj na CPU-GPU interkonekciji.",{},{"id":672,"data":673,"type":609,"tunes":706},"t-0087",{"content":674,"stretched":705,"withHeadings":15},[675,680,685,690,695,700],[676,677,678,679],"Rezultat istraživanja","Prijavljena vrednost","Šta podržava","Šta NE dokazuje",[681,682,683,684],"Naučene UVM performanse","+10,89%","Naučeno predviđanje pristupa može nadmašiti prethodni UVM prefetch baseline","Komercijalna igra dobija 10,89% FPS",[686,687,688,689],"Stopa pogodaka UVM stranica","89,02% vs 76,10%","Predviđanje može materijalno promeniti gde se pronalaze traženi podaci","Svaka tekstura\u002Fstranica u igri je jednako predvidiva",[691,692,693,694],"Saobraćaj na CPU–GPU interkonekciji","−11,05%","Bolji smeštaj može smanjiti reaktivno premeštanje","PCIe saobraćaj u igrama uvek opada",[696,697,698,699],"Efikasnost AAAI attention kernel-a","2,15×","Prefetch može sakriti skup pristup memoriji iza korisnog računanja","Gejming kernel-i dobijaju 2,15×",[701,702,703,704],"AAAI end-to-end propusnost","Do 1,97×","Skrivanje latencije može se propagirati dalje od jednog kernel-a","Multiplikator frame-rate-a igre od 1,97×",false,{},{"id":708,"data":709,"type":552,"tunes":713},"c-0088",{"body":710,"title":711,"variant":712},"Ti rezultati dolaze iz benchmark skupa tog rada, \u003Cstrong>ne iz komercijalnih gejming engine-a\u003C\u002Fstrong>. Dokazuju da naučeno predviđanje pristupa memoriji može funkcionisati u GPU UVM radnim opterećenjima. Ne dokazuju dobitak od 10,89% FPS ili specifično smanjenje seckanja u igri.","Ne kopirajte ove brojeve na igre","warning",{},{"id":715,"data":716,"type":544,"tunes":718},"v3-two-evidence-p",{"text":717},"Studija o naučenom UVM-u je korisna jer predviđa koji je pristup stranici verovatan na osnovu ponašanja memorije. Rad o AAAI KV-kešu iz 2026. je koristan iz drugog razloga: pokazuje da raspoređivač može iskoristiti inače neiskorišćene prozore propusnog opsega da odluči kada da premesti podatke u brži keš pre potražnje. Zajedno podržavaju dva odvojena mehanizma u hipotezi Figure Rocks — predviđanje i tajming — dok nijedan rad nije gejming benchmark.",{},{"id":720,"data":721,"type":572,"tunes":723},"h-0089",{"text":722,"level":47},"Igre imaju dodatne informacije koje generički prefetčer nema",{},{"id":725,"data":726,"type":659,"tunes":750},"flow-0090",{"steps":727,"title":749,"orientation":658},[728,731,734,737,740,743,746],{"label":729,"description":730},"1. Pročitaj determinističko stanje","Smer kamere, pozicija, brzina, graf nivoa, vidljivost, animacija, stanje questa ili susreta.",{"label":732,"description":733},"2. Pročitaj istoriju pristupa","Koje su stranice tekstura, mreže, šejderi, klasteri geometrije i podaci modela nedavno korišćeni.",{"label":735,"description":736},"3. Predvidi radni skup bliske budućnosti","Proceni verovatno potrebne resurse nekoliko frejmova ili sekundi unapred u zavisnosti od cene.",{"label":738,"description":739},"4. Dodeli pouzdanost i cenu","Resurs od 4 MB sa 95% verovatnoće je drugačija odluka od regiona od 2 GB sa 20% verovatnoće.",{"label":741,"description":742},"5. Prefetčuj u okviru budžeta propusnog opsega","Ne dozvoli da samo predviđanje zasiti putanju potrebnu trenutnom renderovanju.",{"label":744,"description":745},"6. Meri promašaje i zagađenje","Prati izbegnute zastoje, pogrešna predviđanja i izbačene korisne podatke.",{"label":747,"description":748},"7. Prilagodi se","Ažuriraj prediktor ili politiku engine-a za stvarnu scenu i ponašanje igrača.","Petlja prediktivne gejming memorije",{},{"id":752,"data":753,"type":552,"tunes":757},"c-0091",{"body":754,"title":755,"variant":756},"Igra ne mora da bira između ručno pisanog strimovanja i AI. \u003Cstrong>Najjači dizajn može biti hibrid:\u003C\u002Fstrong> deterministička pravila garantuju kritične resurse, dok naučeni prediktor rangira opcioni ili bliski budući rad, bira dubinu prefetčovanja i troši samo bezbednu rezervu propusnog opsega. Ako je pouzdanost niska, sistem se vraća na autorizovana pravila.","Zanimljivi hibrid","success",{},{"id":759,"data":760,"type":572,"tunes":762},"v3-known-future",{"text":761,"level":47},"Motor može da kombinuje poznatu budućnost sa predviđenom budućnošću",{},{"id":764,"data":765,"type":617,"tunes":829},"v3-known-pred",{"rows":766,"title":821,"layout":609,"columns":822},[767,773,779,785,791,797,803,809,815],{"id":768,"label":769,"values":770},"camera","Stanje kamere \u002F portala",{"known":771,"learned":772},"Trenutni prikaz, frustum, graf soba, vidljivi portal","U kom smeru će se igrač verovatno sledeće okrenuti",{"id":774,"label":775,"values":776},"movement","Kretanje igrača",{"known":777,"learned":778},"Brzina, unos, navigaciona ograničenja","Verovatna putanja kretanja nekoliko trenutaka unapred",{"id":780,"label":781,"values":782},"assets","Zavisnosti resursa",{"known":783,"learned":784},"Zavisnosti za poznatu ćeliju nivoa ili objekat","Koji opcioni susedni resursi će postati aktivni",{"id":786,"label":787,"values":788},"lod","LOD stanje",{"known":789,"learned":790},"Trenutna udaljenost i pragovi","Koje LOD tranzicije su verovatno uskoro",{"id":792,"label":793,"values":794},"shaders","Graf materijala\u002Fšejdera",{"known":795,"learned":796},"Šejderi potrebni za poznati vidljivi sadržaj","Koje varijante će verovatno biti zatražene sledeće",{"id":798,"label":799,"values":800},"strength","Snaga jezgra",{"known":801,"learned":802},"Poznaje čvrsta pravila nivoa, zavisnosti i garantovane tranzicije","Rangira dvosmislen budući pristup na osnovu ponašanja i konteksta",{"id":804,"label":805,"values":806},"risk","Način otkaza",{"known":807,"learned":808},"Može biti konzervativan i učitavati široke skupove","Može pogrešiti i zagaditi keš ili memoriju",{"id":810,"label":811,"values":812},"best","Najbolja upotreba",{"known":813,"learned":814},"Kritični resursi i garantovane tranzicije","Opciono prioritizovanje unutar ograničenog budžeta propusnog opsega",{"id":816,"label":817,"values":818},"safety","Bezbednosna politika",{"known":819,"learned":820},"Lako je razumeti i testirati","Zahteva pragove pouzdanosti, telemetriju i deterministički fallback","Determinističko znanje naspram naučenog predviđanja",[823,826],{"id":824,"label":825},"known","Motor već zna",{"id":827,"label":828},"learned","Prediktor može da proceni",{},{"id":831,"data":832,"type":572,"tunes":834},"h-0092",{"text":833,"level":47},"Jednostavan primer: trčanje ka vratima",{},{"id":836,"data":837,"type":544,"tunes":839},"p-0093",{"text":838},"Zamislite igrača koji trči niz hodnik ka vratima koja se otvaraju u gustu pijacu. Konvencionalni sistem strimovanja već zna sledeću zonu i može početi da je učitava. Prediktivna memorija dodaje drugi sloj: na osnovu smera kamere, kretanja, prethodnih izbora igrača i trenutnog stanja misije, može rangirati koji podskup tekstura, animacionih podataka NPC-ova, šejdera i geometrije će najverovatnije biti potreban prvi.",{},{"id":841,"data":842,"type":572,"tunes":844},"h-0095",{"text":843,"level":47},"Zašto bi ovo moglo biti važnije kako neuronsko renderovanje raste",{},{"id":846,"data":847,"type":544,"tunes":849},"p-0096",{"text":848},"Neuronska grafika dodaje nove klase podataka: težine modela, latentne ili kompresovane reprezentacije, istorijske bafere i međutenzore. Ako se ti resursi takmiče sa teksturama i geometrijom, rezidentnost postaje komplikovanija. Prediktivno zakazivanje moglo bi da odluči ne samo koji resurs premestiti, već i koju reprezentaciju zadržati: sirove podatke, kompresovanu neuronsku reprezentaciju ili rekonstruktivnu formu.",{},{"id":851,"data":852,"type":626,"tunes":857},"ref-0097",{"url":853,"title":854,"excerpt":855,"ctaLabel":856},"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 nije samo upscaling: šta generativno neuronsko renderovanje zapravo menja","Neuronsko renderovanje menja koje podatke frejm zahteva i kada ih zahteva.","Pročitajte analizu neuronskog renderovanja",{},{"id":859,"data":860,"type":572,"tunes":862},"h-0098",{"text":861,"level":47},"Načini otkaza su jednako važni kao i prilika",{},{"id":864,"data":865,"type":544,"tunes":867},"v3-bandwidth-budget-p",{"text":866},"Prefetch nije besplatan. Pogrešna predikcija može dohvatiti resurse koje igrač nikada ne vidi, izbaciti korisne stranice i takmičiti se sa saobraćajem kritičnim za frejm. Ispravan cilj stoga nije maksimalni prefetch; to je maksimalna korisna stopa pogodaka unutar ograničenog budžeta propusnog opsega i rezidentnosti.",{},{"id":869,"data":870,"type":609,"tunes":899},"t-0099",{"content":871,"stretched":705,"withHeadings":15},[872,875,879,883,887,891,895],[805,873,874],"Šta se dešava","Ideja za ublažavanje",[876,877,878],"Loša predikcija","Pogrešni podaci se prefetch-uju","Pragovi pouzdanosti; rezervisati budžet za determinističku potražnju",[880,881,882],"Zagađenje keša","Korisni trenutni podaci se izbacuju","Odvojiti predviđene i zaštićene klase rezidentnosti",[884,885,886],"Nadmetanje za propusni opseg","Prefetch krade propusni opseg od trenutnog frejma","Zakazivati samo u prozorima sa slobodnim kapacitetom; sprovesti budžet po frejmu",[888,889,890],"Promena ponašanja","Igrač se okreće ili brzo putuje","Jeftino otkazivanje i brzi fallback",[892,893,894],"Trošak modela","Prediktor košta više nego što štedi","Mali modeli, offline obuka ili lagani prediktori izvedeni iz pravila",[896,897,898],"Zastoj koji nije memorijski","Problem ostaje","Držati dijagnostiku odvojeno: šejder, CPU, mreža i putanje skladištenja zahtevaju sopstvene testove",{},{"id":901,"data":902,"type":572,"tunes":904},"h-0100",{"text":903,"level":47},"Kako biste to testirali u stvarnom motoru",{},{"id":906,"data":907,"type":659,"tunes":931},"flow-0101",{"steps":908,"title":930,"orientation":658},[909,912,915,918,921,924,927],{"label":910,"description":911},"A. Uspostaviti osnovu","Zabeležiti skokove vremena frejma, page fault-ove\u002Fmigracije, promašaje rezidentnosti i obim prenosa podataka bez predikcije.",{"label":913,"description":914},"B. Dodati samo deterministički prefetch","Izmeriti šta konvencionalno znanje motora već rešava.",{"label":916,"description":917},"C. Dodati naučeno rangiranje","Koristiti isti budžet memorije i propusnog opsega kako bi poređenje bilo fer.",{"label":919,"description":920},"D. Izmeriti stopu pogodaka i zagađenje","Pratiti korisne prefetch-ovane bajtove naspram izgubljenih bajtova i izbacivanja.",{"label":922,"description":923},"E. Izmeriti distribuciju vremena frejma","Prosečan FPS nije dovoljan; ispitati repnu latenciju i učestalost zastoja.",{"label":925,"description":926},"F. Testirati promene ponašanja pod stresom","Brzi zaokreti, teleport, nepredvidivost u multiplayer-u i proceduralne scene.",{"label":928,"description":929},"G. Uporediti trošak","Vreme prediktora na CPU\u002FGPU + memorijski overhead mora biti niže od zastoja koje uklanja.","Evaluacija prediktivne memorije",{},{"id":933,"data":934,"type":572,"tunes":936},"h-0102",{"text":935,"level":47},"Gde se ova hipoteza zaustavlja",{},{"id":938,"data":939,"type":544,"tunes":941},"p-0103",{"text":940},"Prediktivna memorija ne može da učini premali memorijski pool beskonačno velikim. Ne može da stvori propusni opseg. Ne može da popravi zastoj pri kompilaciji šejdera pogađanjem sledeće teksture. I postaje manje korisna kada je budućnost igrača namerno nepredvidiva ili kada motor već ima skoro savršene determinističke informacije o strimovanju.",{},{"id":943,"data":944,"type":572,"tunes":946},"h-0104",{"text":945,"level":47},"Lagana veza sa opštom AI arhitekturom",{},{"id":948,"data":949,"type":544,"tunes":951},"p-0105",{"text":950},"Dublji obrazac je sličan retrieval sistemima izvan gejminga: sistem ne treba da dohvata sve, i ne treba da čeka do poslednjeg trenutka da otkrije da kontekst nedostaje. Potreban mu je trigger, skup kandidata, politika rangiranja i granica validnosti. Gejming jednostavno nameće mnogo stroži budžet latencije.",{},{"id":953,"data":954,"type":626,"tunes":959},"ref-0106",{"url":955,"title":956,"excerpt":957,"ctaLabel":958},"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","Šta je RAG? Najjednostavnije objašnjenje kako funkcioniše","Opšte objašnjenje retrieval-a, konteksta i zašto sistemi dohvataju informacije umesto da sve odjednom smeste u model.","Pročitajte RAG objašnjenje na stajic.de",{},{"id":961,"data":962,"type":572,"tunes":964},"h-0107",{"text":963,"level":47},"Primarni izvor",{},{"id":966,"data":967,"type":973,"tunes":974},"src-0108",{"link":968,"meta":969},"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490",{"image":970,"title":971,"description":972},{"url":13},"Deep learning based data prefetching in CPU-GPU Unified Virtual Memory","Research paper reporting learned UVM page-prefetching results across 11 memory-intensive benchmarks.","linkTool",{},{"id":976,"data":977,"type":973,"tunes":983},"v3-src-aaai2",{"link":978,"meta":979},"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224",{"image":980,"title":981,"description":982},{"url":13},"AAAI 2026 — Asynchronous KV Cache Prefetching","Published 2026 evidence for proactively using idle memory bandwidth and L2 cache prefetch.",{},{"id":985,"data":986,"type":1009,"tunes":1010},"faq-0109",{"items":987,"title":1008},[988,992,996,1000,1004],{"id":989,"answer":990,"question":991},"faq1","Not in the specific Figure Rocks architecture proposed here. Games already use many forms of prefetching and predictive streaming; the hypothesis is to combine deterministic game state with learned GPU-memory prediction.","Is AI prefetching already a standard game-engine feature?",{"id":993,"answer":994,"question":995},"faq2","No. It aims to improve when and what data is moved so the processor waits less often.","Would this increase RAM or VRAM speed?",{"id":997,"answer":998,"question":999},"faq3","No. It can only address stalls caused by data availability\u002Fresidency when prediction is accurate enough.","Could it eliminate stutter?",{"id":1001,"answer":1002,"question":1003},"faq4","Because engines expose structured information about camera, level topology, assets, animation and likely visibility that a generic application does not.","Why might games be a good prediction target?",{"id":1005,"answer":1006,"question":1007},"faq5","Not one metric. Useful evaluation needs page\u002Fresource hit rate, bandwidth overhead, cache pollution and frame-time tail latency.","What metric matters most?","Predictive gaming memory","faq",{},{"id":1012,"data":1013,"type":1032,"tunes":1033},"gloss-0110",{"title":1014,"entries":1015},"Predictive memory terms",[1016,1020,1024,1028],{"term":1017,"anchor":1018,"definition":1019},"Prefetch","prefetch","Moving or making data available before explicit demand.",{"term":1021,"anchor":1022,"definition":1023},"Residency","residency","Keeping the resource in memory close enough to the processor that it can be used without an expensive transfer.",{"term":1025,"anchor":1026,"definition":1027},"Cache pollution","cache-pollution","Replacing useful data with prefetched data that turns out not to be needed.",{"term":1029,"anchor":1030,"definition":1031},"Predictive Gaming Memory","predictive-gaming-memory","Figure Rocks hypothesis combining deterministic engine state, learned prediction, prefetch and measured residency policy.","glossary",{},"2.31","Gejm endžini već znaju kuda idu igrač, kamera i svet. Istraživanja pokazuju da naučeno GPU prefetčovanje može da predvidi memorijske stranice. Šta se dešava ako se te ideje kombinuju?","\u002Fuploads\u002F2026\u002F10\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter-1792339100514-lyx0yw.webp","what-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter-1792339100514-lyx0yw","PUBLISHED","2026-10-18T11:57:00.000Z","2026-10-18T15:57:27.289Z","2026-10-18T16:01:40.667Z",{"en":1043,"de":1044,"sr":1045,"es":1046,"fr":1047,"it":1048,"ru":1049,"zh":1050},"\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fde\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fsr\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fes\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Ffr\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fit\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fru\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fzh\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter",[1052,1056,1060,1064,1068,1072,1076],{"id":1053,"name":1054,"slug":1055},144,"Технологија","tech",{"id":1057,"name":1058,"slug":1059},170,"Технолошке вести","tech-news",{"id":1061,"name":1062,"slug":1063},280,"Хронологија технологије екрана","display-tech-timeline",{"id":1065,"name":1066,"slug":1067},153,"Технологија екрана","display-tech",{"id":1069,"name":1070,"slug":1071},165,"Вести","news",{"id":1073,"name":1074,"slug":1075},166,"Гејминг вести","gaming-news",{"id":1077,"name":1078,"slug":1079},427,"Ново","new",{"id":283,"login":1081,"email":13,"displayName":1082},"aleksandar","Aleksandar Stajic",[1084,1473],{"lang":8,"title":1085,"content":1086,"contentJson":1087,"excerpt":1472},"What If a Game Could Predict Its Next Memory Access? AI Prefetching and the Future of Stutter","{\"time\":1792339102894,\"blocks\":[{\"id\":\"p-0075\",\"type\":\"paragraph\",\"data\":{\"text\":\"A game rarely knows the exact future, but it knows much more about the next few frames than a general-purpose application does. It knows the camera vector, player velocity, visible cells, nearby portals, asset dependencies, animation state and often which shaders or effects are likely to run next. That makes gaming an unusually interesting target for predictive memory management.\"},\"tunes\":{}},{\"id\":\"c-0076\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>AI-assisted prefetching could plausibly reduce some memory-related stalls, but it would not be a universal anti-stutter button.\u003C\u002Fstrong> Research already shows deep-learning prefetchers improving CPU-GPU unified-memory page prediction. A game engine could add something those benchmarks do not have: explicit world and player state. The Figure Rocks hypothesis is to combine both.\"},\"tunes\":{}},{\"id\":\"c-0077\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Figure Rocks hypothesis, not an announced feature\",\"body\":\"The term \u003Cstrong>Predictive Gaming Memory\u003C\u002Fstrong> in this article describes a proposed architecture: deterministic engine knowledge + learned access prediction + prefetch + residency policy. It is not an NVIDIA, Microsoft, Epic or AMD product name.\"},\"tunes\":{}},{\"id\":\"toc-0078\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-0079\",\"type\":\"header\",\"data\":{\"text\":\"First separate memory stalls from other stutter\",\"level\":2},\"tunes\":{}},{\"id\":\"cmp-0080\",\"type\":\"comparison\",\"data\":{\"title\":\"Stutter sources that can look similar\",\"layout\":\"table\",\"columns\":[{\"id\":\"memory\",\"label\":\"Memory\u002Fdata path\"},{\"id\":\"other\",\"label\":\"Other path\"}],\"rows\":[{\"id\":\"asset\",\"label\":\"Asset missing from fast memory\",\"values\":{\"memory\":\"Possible\",\"other\":\"Storage or decompression can also be involved\"}},{\"id\":\"shader\",\"label\":\"Shader not ready\",\"values\":{\"memory\":\"May involve caches\",\"other\":\"Compilation\u002Fpipeline creation can be the real cause\"}},{\"id\":\"cpu\",\"label\":\"CPU simulation spike\",\"values\":{\"memory\":\"Cache\u002Fmemory behavior can contribute\",\"other\":\"Game logic or draw-call work may dominate\"}},{\"id\":\"network\",\"label\":\"Online hitch\",\"values\":{\"memory\":\"Usually not the primary cause\",\"other\":\"Server or packet timing can dominate\"}},{\"id\":\"vram\",\"label\":\"Eviction\u002Fresidency pressure\",\"values\":{\"memory\":\"Directly relevant\",\"other\":\"Can be misdiagnosed from allocation meters alone\"}}]},\"tunes\":{}},{\"id\":\"ref-0081\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it\",\"title\":\"Why PC Games Stutter When Compiling Shaders — and How Advanced Shader Delivery Changes It\",\"excerpt\":\"Shader stutter is a different problem from memory-page prediction and should not be collapsed into one diagnosis.\",\"ctaLabel\":\"Separate shader stutter from memory stalls\"},\"tunes\":{}},{\"id\":\"h-0082\",\"type\":\"header\",\"data\":{\"text\":\"Prefetching already exists; the new question is how it predicts\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0083\",\"type\":\"paragraph\",\"data\":{\"text\":\"Traditional prefetching tries to move data before a processor explicitly asks for it. Hardware prefetchers exploit access patterns. Engines stream world cells before the player reaches them. Operating systems and runtimes keep likely-needed pages hot. The difficult part is false prediction: prefetch too little and you still stall; prefetch too much and you waste bandwidth and evict useful data.\"},\"tunes\":{}},{\"id\":\"flow-0084\",\"type\":\"processFlow\",\"data\":{\"title\":\"The basic prefetch trade-off\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Observe\",\"description\":\"Collect recent accesses, world state and workload context.\"},{\"label\":\"Predict\",\"description\":\"Estimate which pages\u002Fassets\u002Fshaders will be needed next.\"},{\"label\":\"Move\",\"description\":\"Transfer or make those resources resident before demand.\"},{\"label\":\"Use\",\"description\":\"The GPU consumes the resource without waiting if prediction was correct.\"},{\"label\":\"Correct\",\"description\":\"Evict wrong predictions and update the model\u002Fpolicy.\"}]},\"tunes\":{}},{\"id\":\"h-0085\",\"type\":\"header\",\"data\":{\"text\":\"There is already research evidence for learned GPU prefetching\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0086\",\"type\":\"paragraph\",\"data\":{\"text\":\"A published study on deep-learning-based prefetching for CPU-GPU Unified Virtual Memory used a Transformer to model global memory-access context and then derived a lighter predictor. Across 11 memory-intensive benchmarks, the paper reported 10.89% performance improvement over the compared framework, a device-memory page hit rate of 89.02% versus 76.10%, and 11.05% lower CPU-GPU interconnect traffic.\"},\"tunes\":{}},{\"id\":\"t-0087\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Research result\",\"Reported value\",\"What it supports\",\"What it does NOT prove\"],[\"Learned UVM performance\",\"+10.89%\",\"Learned access prediction can beat a prior UVM prefetch baseline\",\"A commercial game gains 10.89% FPS\"],[\"UVM page hit rate\",\"89.02% vs 76.10%\",\"Prediction can materially change where requested data is found\",\"Every game texture\u002Fpage is equally predictable\"],[\"CPU–GPU interconnect traffic\",\"−11.05%\",\"Better placement can reduce reactive movement\",\"PCIe traffic always falls in games\"],[\"AAAI attention-kernel efficiency\",\"2.15×\",\"Prefetch can hide expensive memory access behind useful compute\",\"Gaming kernels receive 2.15×\"],[\"AAAI end-to-end throughput\",\"Up to 1.97×\",\"Latency hiding can propagate beyond one kernel\",\"A 1.97× game frame-rate multiplier\"]]},\"tunes\":{}},{\"id\":\"c-0088\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"Do not copy these numbers onto games\",\"body\":\"Those results come from the paper's benchmark set, \u003Cstrong>not from commercial game engines\u003C\u002Fstrong>. They prove that learned memory-access prediction can work in GPU UVM workloads. They do not prove a 10.89% FPS gain or a specific stutter reduction in a game.\"},\"tunes\":{}},{\"id\":\"v3-two-evidence-p\",\"type\":\"paragraph\",\"data\":{\"text\":\"The learned-UVM study is useful because it predicts what page access is likely from memory behavior. The 2026 AAAI KV-cache work is useful for a different reason: it shows that a scheduler can exploit otherwise idle bandwidth windows to decide when to move data into a faster cache before demand. Together they support two separate mechanisms in the Figure Rocks hypothesis — prediction and timing — while neither paper is a gaming benchmark.\"},\"tunes\":{}},{\"id\":\"h-0089\",\"type\":\"header\",\"data\":{\"text\":\"Games have extra information a generic prefetcher does not\",\"level\":2},\"tunes\":{}},{\"id\":\"flow-0090\",\"type\":\"processFlow\",\"data\":{\"title\":\"The Predictive Gaming Memory Loop\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Read deterministic state\",\"description\":\"Camera direction, position, velocity, level graph, visibility, animation, quest or encounter state.\"},{\"label\":\"2. Read access history\",\"description\":\"Which texture pages, meshes, shaders, geometry clusters and model data were recently used.\"},{\"label\":\"3. Predict the near-future working set\",\"description\":\"Estimate likely-needed resources several frames or seconds ahead depending on cost.\"},{\"label\":\"4. Assign confidence and cost\",\"description\":\"A 95% likely 4 MB asset is a different decision from a 20% likely 2 GB region.\"},{\"label\":\"5. Prefetch within a bandwidth budget\",\"description\":\"Do not let prediction itself saturate the path needed by current rendering.\"},{\"label\":\"6. Measure misses and pollution\",\"description\":\"Track stalls avoided, wrong predictions and useful data evicted.\"},{\"label\":\"7. Adapt\",\"description\":\"Update the predictor or engine policy for the actual scene and player behavior.\"}]},\"tunes\":{}},{\"id\":\"c-0091\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The interesting hybrid\",\"body\":\"A game does not have to choose between hand-authored streaming and AI. \u003Cstrong>The strongest design may be hybrid:\u003C\u002Fstrong> deterministic rules guarantee critical resources, while a learned predictor ranks optional or near-future work, chooses prefetch depth and spends only safe bandwidth headroom. If confidence is low, the system falls back to authored rules.\"},\"tunes\":{}},{\"id\":\"v3-known-future\",\"type\":\"header\",\"data\":{\"text\":\"The engine can combine known future with predicted future\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-known-pred\",\"type\":\"comparison\",\"data\":{\"title\":\"Deterministic knowledge vs learned prediction\",\"layout\":\"table\",\"columns\":[{\"id\":\"known\",\"label\":\"Engine already knows\"},{\"id\":\"learned\",\"label\":\"Predictor can estimate\"}],\"rows\":[{\"id\":\"camera\",\"label\":\"Camera \u002F portal state\",\"values\":{\"known\":\"Current view, frustum, room graph, visible portal\",\"learned\":\"Which direction the player is likely to turn next\"}},{\"id\":\"movement\",\"label\":\"Player movement\",\"values\":{\"known\":\"Velocity, input, navigation constraints\",\"learned\":\"Likely traversal path several moments ahead\"}},{\"id\":\"assets\",\"label\":\"Asset dependencies\",\"values\":{\"known\":\"Dependencies for a known level cell or object\",\"learned\":\"Which optional neighboring assets will become hot\"}},{\"id\":\"lod\",\"label\":\"LOD state\",\"values\":{\"known\":\"Current distance and thresholds\",\"learned\":\"Which LOD transitions are likely soon\"}},{\"id\":\"shaders\",\"label\":\"Material\u002Fshader graph\",\"values\":{\"known\":\"Shaders required by known visible content\",\"learned\":\"Which variants are likely to be requested next\"}},{\"id\":\"strength\",\"label\":\"Core strength\",\"values\":{\"known\":\"Knows hard level rules, dependencies and guaranteed transitions\",\"learned\":\"Ranks ambiguous future access from behavior and context\"}},{\"id\":\"risk\",\"label\":\"Failure mode\",\"values\":{\"known\":\"Can be conservative and load broad sets\",\"learned\":\"Can be wrong and pollute cache or memory\"}},{\"id\":\"best\",\"label\":\"Best use\",\"values\":{\"known\":\"Critical assets and guaranteed transitions\",\"learned\":\"Optional prioritization inside a bounded bandwidth budget\"}},{\"id\":\"safety\",\"label\":\"Safety policy\",\"values\":{\"known\":\"Easy to reason about and test\",\"learned\":\"Needs confidence thresholds, telemetry and deterministic fallback\"}}]},\"tunes\":{}},{\"id\":\"h-0092\",\"type\":\"header\",\"data\":{\"text\":\"A simple example: running toward a door\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0093\",\"type\":\"paragraph\",\"data\":{\"text\":\"Imagine a player sprinting down a corridor toward a door that opens into a dense market square. A conventional streaming system already knows the next zone and can begin loading it. Predictive memory adds a second layer: based on camera direction, movement, prior player choices and current mission state, it can rank which subset of textures, NPC animation data, shaders and geometry is most likely to be needed first.\"},\"tunes\":{}},{\"id\":\"h-0095\",\"type\":\"header\",\"data\":{\"text\":\"Why this could matter more as neural rendering grows\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0096\",\"type\":\"paragraph\",\"data\":{\"text\":\"Neural graphics adds new data classes: model weights, latent or compressed representations, history buffers and intermediate tensors. If those resources compete with textures and geometry, residency becomes more complicated. Predictive scheduling could decide not only which asset to move, but which representation to keep: raw data, a compressed neural representation, or a reconstructable form.\"},\"tunes\":{}},{\"id\":\"ref-0097\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\",\"title\":\"DLSS 5 Is Not Just Upscaling: What Generative Neural Rendering Actually Changes\",\"excerpt\":\"Neural rendering changes what data a frame needs and when it needs it.\",\"ctaLabel\":\"Read the neural rendering analysis\"},\"tunes\":{}},{\"id\":\"h-0098\",\"type\":\"header\",\"data\":{\"text\":\"The failure modes are as important as the opportunity\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-bandwidth-budget-p\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prefetch is not free. A wrong prediction can fetch resources the player never sees, evict useful pages and compete with frame-critical traffic. The correct goal is therefore not maximum prefetch; it is maximum useful hit rate inside a bounded bandwidth and residency budget.\"},\"tunes\":{}},{\"id\":\"t-0099\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Failure mode\",\"What happens\",\"Mitigation idea\"],[\"Bad prediction\",\"Wrong data is prefetched\",\"Confidence thresholds; reserve budget for deterministic demand\"],[\"Cache pollution\",\"Useful current data is evicted\",\"Separate predicted and protected residency classes\"],[\"Bandwidth contention\",\"Prefetch steals bandwidth from current frame\",\"Schedule only in headroom windows; enforce per-frame budget\"],[\"Behavior shift\",\"Player turns around or fast-travels\",\"Cheap cancellation and rapid fallback\"],[\"Model overhead\",\"Predictor costs more than it saves\",\"Small models, offline training, or rule-derived lightweight predictors\"],[\"Non-memory stutter\",\"Problem remains\",\"Keep diagnosis separate: shader, CPU, network and storage paths need their own tests\"]]},\"tunes\":{}},{\"id\":\"h-0100\",\"type\":\"header\",\"data\":{\"text\":\"How you would test it in a real engine\",\"level\":2},\"tunes\":{}},{\"id\":\"flow-0101\",\"type\":\"processFlow\",\"data\":{\"title\":\"Predictive Memory Evaluation\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"A. Establish baseline\",\"description\":\"Capture frame-time spikes, page faults\u002Fmigrations, residency misses and data-transfer volume without prediction.\"},{\"label\":\"B. Add deterministic prefetch only\",\"description\":\"Measure what conventional engine knowledge already solves.\"},{\"label\":\"C. Add learned ranking\",\"description\":\"Use the same memory and bandwidth budget so the comparison is fair.\"},{\"label\":\"D. Measure hit rate and pollution\",\"description\":\"Track useful prefetched bytes versus wasted bytes and evictions.\"},{\"label\":\"E. Measure frame-time distribution\",\"description\":\"Average FPS is not enough; inspect tail latency and hitch frequency.\"},{\"label\":\"F. Stress behavior changes\",\"description\":\"Fast turns, teleport, multiplayer unpredictability and procedural scenes.\"},{\"label\":\"G. Compare cost\",\"description\":\"Predictor CPU\u002FGPU time + memory overhead must be lower than the stalls it removes.\"}]},\"tunes\":{}},{\"id\":\"h-0102\",\"type\":\"header\",\"data\":{\"text\":\"Where this hypothesis stops\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0103\",\"type\":\"paragraph\",\"data\":{\"text\":\"Predictive memory cannot make a too-small memory pool infinitely large. It cannot create bandwidth. It cannot fix a shader compilation hitch by guessing the next texture. And it becomes less useful when the player's future is intentionally unpredictable or when the engine already has near-perfect deterministic streaming information.\"},\"tunes\":{}},{\"id\":\"h-0104\",\"type\":\"header\",\"data\":{\"text\":\"A light connection to general AI architecture\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0105\",\"type\":\"paragraph\",\"data\":{\"text\":\"The deeper pattern is similar to retrieval systems outside gaming: the system should not fetch everything, and it should not wait until the final moment to discover that context is missing. It needs a trigger, a candidate set, a ranking policy and a validity boundary. Gaming simply imposes a much tighter latency budget.\"},\"tunes\":{}},{\"id\":\"ref-0106\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works\",\"title\":\"What Is RAG? The Simplest Explanation of How It Works\",\"excerpt\":\"A general explanation of retrieval, context and why systems fetch information instead of placing everything in the model at once.\",\"ctaLabel\":\"Read the RAG explainer on stajic.de\"},\"tunes\":{}},{\"id\":\"h-0107\",\"type\":\"header\",\"data\":{\"text\":\"Primary source\",\"level\":2},\"tunes\":{}},{\"id\":\"src-0108\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\",\"meta\":{\"title\":\"Deep learning based data prefetching in CPU-GPU Unified Virtual Memory\",\"description\":\"Research paper reporting learned UVM page-prefetching results across 11 memory-intensive benchmarks.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v3-src-aaai2\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224\",\"meta\":{\"title\":\"AAAI 2026 — Asynchronous KV Cache Prefetching\",\"description\":\"Published 2026 evidence for proactively using idle memory bandwidth and L2 cache prefetch.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"faq-0109\",\"type\":\"faq\",\"data\":{\"title\":\"Predictive gaming memory\",\"items\":[{\"id\":\"faq1\",\"question\":\"Is AI prefetching already a standard game-engine feature?\",\"answer\":\"Not in the specific Figure Rocks architecture proposed here. Games already use many forms of prefetching and predictive streaming; the hypothesis is to combine deterministic game state with learned GPU-memory prediction.\"},{\"id\":\"faq2\",\"question\":\"Would this increase RAM or VRAM speed?\",\"answer\":\"No. It aims to improve when and what data is moved so the processor waits less often.\"},{\"id\":\"faq3\",\"question\":\"Could it eliminate stutter?\",\"answer\":\"No. It can only address stalls caused by data availability\u002Fresidency when prediction is accurate enough.\"},{\"id\":\"faq4\",\"question\":\"Why might games be a good prediction target?\",\"answer\":\"Because engines expose structured information about camera, level topology, assets, animation and likely visibility that a generic application does not.\"},{\"id\":\"faq5\",\"question\":\"What metric matters most?\",\"answer\":\"Not one metric. Useful evaluation needs page\u002Fresource hit rate, bandwidth overhead, cache pollution and frame-time tail latency.\"}]},\"tunes\":{}},{\"id\":\"gloss-0110\",\"type\":\"glossary\",\"data\":{\"title\":\"Predictive memory terms\",\"entries\":[{\"term\":\"Prefetch\",\"definition\":\"Moving or making data available before explicit demand.\",\"anchor\":\"prefetch\"},{\"term\":\"Residency\",\"definition\":\"Keeping the resource in memory close enough to the processor that it can be used without an expensive transfer.\",\"anchor\":\"residency\"},{\"term\":\"Cache pollution\",\"definition\":\"Replacing useful data with prefetched data that turns out not to be needed.\",\"anchor\":\"cache-pollution\"},{\"term\":\"Predictive Gaming Memory\",\"definition\":\"Figure Rocks hypothesis combining deterministic engine state, learned prediction, prefetch and measured residency policy.\",\"anchor\":\"predictive-gaming-memory\"}]},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1088,"blocks":1089,"version":1471},1792339102894,[1090,1094,1099,1104,1108,1112,1147,1154,1158,1162,1182,1186,1190,1224,1229,1233,1237,1263,1268,1272,1327,1331,1335,1339,1343,1350,1354,1358,1388,1392,1418,1422,1426,1430,1434,1440,1444,1449,1454,1463],{"id":541,"data":1091,"type":544,"tunes":1093},{"text":1092},"A game rarely knows the exact future, but it knows much more about the next few frames than a general-purpose application does. It knows the camera vector, player velocity, visible cells, nearby portals, asset dependencies, animation state and often which shaders or effects are likely to run next. That makes gaming an unusually interesting target for predictive memory management.",{},{"id":547,"data":1095,"type":552,"tunes":1098},{"body":1096,"title":1097,"variant":551},"\u003Cstrong>AI-assisted prefetching could plausibly reduce some memory-related stalls, but it would not be a universal anti-stutter button.\u003C\u002Fstrong> Research already shows deep-learning prefetchers improving CPU-GPU unified-memory page prediction. A game engine could add something those benchmarks do not have: explicit world and player state. The Figure Rocks hypothesis is to combine both.","Direct answer",{},{"id":555,"data":1100,"type":552,"tunes":1103},{"body":1101,"title":1102,"variant":559},"The term \u003Cstrong>Predictive Gaming Memory\u003C\u002Fstrong> in this article describes a proposed architecture: deterministic engine knowledge + learned access prediction + prefetch + residency policy. It is not an NVIDIA, Microsoft, Epic or AMD product name.","Figure Rocks hypothesis, not an announced feature",{},{"id":562,"data":1105,"type":566,"tunes":1107},{"title":1106,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1109,"type":572,"tunes":1111},{"text":1110,"level":47},"First separate memory stalls from other stutter",{},{"id":575,"data":1113,"type":617,"tunes":1146},{"rows":1114,"title":1140,"layout":609,"columns":1141},[1115,1120,1125,1130,1135],{"id":579,"label":1116,"values":1117},"Asset missing from fast memory",{"other":1118,"memory":1119},"Storage or decompression can also be involved","Possible",{"id":585,"label":1121,"values":1122},"Shader not ready",{"other":1123,"memory":1124},"Compilation\u002Fpipeline creation can be the real cause","May involve caches",{"id":591,"label":1126,"values":1127},"CPU simulation spike",{"other":1128,"memory":1129},"Game logic or draw-call work may dominate","Cache\u002Fmemory behavior can contribute",{"id":597,"label":1131,"values":1132},"Online hitch",{"other":1133,"memory":1134},"Server or packet timing can dominate","Usually not the primary cause",{"id":603,"label":1136,"values":1137},"Eviction\u002Fresidency pressure",{"other":1138,"memory":1139},"Can be misdiagnosed from allocation meters alone","Directly relevant","Stutter sources that can look similar",[1142,1144],{"id":612,"label":1143},"Memory\u002Fdata path",{"id":615,"label":1145},"Other path",{},{"id":620,"data":1148,"type":626,"tunes":1153},{"url":1149,"title":1150,"excerpt":1151,"ctaLabel":1152},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it","Why PC Games Stutter When Compiling Shaders — and How Advanced Shader Delivery Changes It","Shader stutter is a different problem from memory-page prediction and should not be collapsed into one diagnosis.","Separate shader stutter from memory stalls",{},{"id":629,"data":1155,"type":572,"tunes":1157},{"text":1156,"level":47},"Prefetching already exists; the new question is how it predicts",{},{"id":634,"data":1159,"type":544,"tunes":1161},{"text":1160},"Traditional prefetching tries to move data before a processor explicitly asks for it. Hardware prefetchers exploit access patterns. Engines stream world cells before the player reaches them. Operating systems and runtimes keep likely-needed pages hot. The difficult part is false prediction: prefetch too little and you still stall; prefetch too much and you waste bandwidth and evict useful data.",{},{"id":639,"data":1163,"type":659,"tunes":1181},{"steps":1164,"title":1180,"orientation":658},[1165,1168,1171,1174,1177],{"label":1166,"description":1167},"Observe","Collect recent accesses, world state and workload context.",{"label":1169,"description":1170},"Predict","Estimate which pages\u002Fassets\u002Fshaders will be needed next.",{"label":1172,"description":1173},"Move","Transfer or make those resources resident before demand.",{"label":1175,"description":1176},"Use","The GPU consumes the resource without waiting if prediction was correct.",{"label":1178,"description":1179},"Correct","Evict wrong predictions and update the model\u002Fpolicy.","The basic prefetch trade-off",{},{"id":662,"data":1183,"type":572,"tunes":1185},{"text":1184,"level":47},"There is already research evidence for learned GPU prefetching",{},{"id":667,"data":1187,"type":544,"tunes":1189},{"text":1188},"A published study on deep-learning-based prefetching for CPU-GPU Unified Virtual Memory used a Transformer to model global memory-access context and then derived a lighter predictor. Across 11 memory-intensive benchmarks, the paper reported 10.89% performance improvement over the compared framework, a device-memory page hit rate of 89.02% versus 76.10%, and 11.05% lower CPU-GPU interconnect traffic.",{},{"id":672,"data":1191,"type":609,"tunes":1223},{"content":1192,"stretched":705,"withHeadings":15},[1193,1198,1203,1208,1213,1218],[1194,1195,1196,1197],"Research result","Reported value","What it supports","What it does NOT prove",[1199,1200,1201,1202],"Learned UVM performance","+10.89%","Learned access prediction can beat a prior UVM prefetch baseline","A commercial game gains 10.89% FPS",[1204,1205,1206,1207],"UVM page hit rate","89.02% vs 76.10%","Prediction can materially change where requested data is found","Every game texture\u002Fpage is equally predictable",[1209,1210,1211,1212],"CPU–GPU interconnect traffic","−11.05%","Better placement can reduce reactive movement","PCIe traffic always falls in games",[1214,1215,1216,1217],"AAAI attention-kernel efficiency","2.15×","Prefetch can hide expensive memory access behind useful compute","Gaming kernels receive 2.15×",[1219,1220,1221,1222],"AAAI end-to-end throughput","Up to 1.97×","Latency hiding can propagate beyond one kernel","A 1.97× game frame-rate multiplier",{},{"id":708,"data":1225,"type":552,"tunes":1228},{"body":1226,"title":1227,"variant":712},"Those results come from the paper's benchmark set, \u003Cstrong>not from commercial game engines\u003C\u002Fstrong>. They prove that learned memory-access prediction can work in GPU UVM workloads. They do not prove a 10.89% FPS gain or a specific stutter reduction in a game.","Do not copy these numbers onto games",{},{"id":715,"data":1230,"type":544,"tunes":1232},{"text":1231},"The learned-UVM study is useful because it predicts what page access is likely from memory behavior. The 2026 AAAI KV-cache work is useful for a different reason: it shows that a scheduler can exploit otherwise idle bandwidth windows to decide when to move data into a faster cache before demand. Together they support two separate mechanisms in the Figure Rocks hypothesis — prediction and timing — while neither paper is a gaming benchmark.",{},{"id":720,"data":1234,"type":572,"tunes":1236},{"text":1235,"level":47},"Games have extra information a generic prefetcher does not",{},{"id":725,"data":1238,"type":659,"tunes":1262},{"steps":1239,"title":1261,"orientation":658},[1240,1243,1246,1249,1252,1255,1258],{"label":1241,"description":1242},"1. Read deterministic state","Camera direction, position, velocity, level graph, visibility, animation, quest or encounter state.",{"label":1244,"description":1245},"2. Read access history","Which texture pages, meshes, shaders, geometry clusters and model data were recently used.",{"label":1247,"description":1248},"3. Predict the near-future working set","Estimate likely-needed resources several frames or seconds ahead depending on cost.",{"label":1250,"description":1251},"4. Assign confidence and cost","A 95% likely 4 MB asset is a different decision from a 20% likely 2 GB region.",{"label":1253,"description":1254},"5. Prefetch within a bandwidth budget","Do not let prediction itself saturate the path needed by current rendering.",{"label":1256,"description":1257},"6. Measure misses and pollution","Track stalls avoided, wrong predictions and useful data evicted.",{"label":1259,"description":1260},"7. Adapt","Update the predictor or engine policy for the actual scene and player behavior.","The Predictive Gaming Memory Loop",{},{"id":752,"data":1264,"type":552,"tunes":1267},{"body":1265,"title":1266,"variant":756},"A game does not have to choose between hand-authored streaming and AI. \u003Cstrong>The strongest design may be hybrid:\u003C\u002Fstrong> deterministic rules guarantee critical resources, while a learned predictor ranks optional or near-future work, chooses prefetch depth and spends only safe bandwidth headroom. If confidence is low, the system falls back to authored rules.","The interesting hybrid",{},{"id":759,"data":1269,"type":572,"tunes":1271},{"text":1270,"level":47},"The engine can combine known future with predicted future",{},{"id":764,"data":1273,"type":617,"tunes":1326},{"rows":1274,"title":1320,"layout":609,"columns":1321},[1275,1280,1285,1290,1295,1300,1305,1310,1315],{"id":768,"label":1276,"values":1277},"Camera \u002F portal state",{"known":1278,"learned":1279},"Current view, frustum, room graph, visible portal","Which direction the player is likely to turn next",{"id":774,"label":1281,"values":1282},"Player movement",{"known":1283,"learned":1284},"Velocity, input, navigation constraints","Likely traversal path several moments ahead",{"id":780,"label":1286,"values":1287},"Asset dependencies",{"known":1288,"learned":1289},"Dependencies for a known level cell or object","Which optional neighboring assets will become hot",{"id":786,"label":1291,"values":1292},"LOD state",{"known":1293,"learned":1294},"Current distance and thresholds","Which LOD transitions are likely soon",{"id":792,"label":1296,"values":1297},"Material\u002Fshader graph",{"known":1298,"learned":1299},"Shaders required by known visible content","Which variants are likely to be requested next",{"id":798,"label":1301,"values":1302},"Core strength",{"known":1303,"learned":1304},"Knows hard level rules, dependencies and guaranteed transitions","Ranks ambiguous future access from behavior and context",{"id":804,"label":1306,"values":1307},"Failure mode",{"known":1308,"learned":1309},"Can be conservative and load broad sets","Can be wrong and pollute cache or memory",{"id":810,"label":1311,"values":1312},"Best use",{"known":1313,"learned":1314},"Critical assets and guaranteed transitions","Optional prioritization inside a bounded bandwidth budget",{"id":816,"label":1316,"values":1317},"Safety policy",{"known":1318,"learned":1319},"Easy to reason about and test","Needs confidence thresholds, telemetry and deterministic fallback","Deterministic knowledge vs learned prediction",[1322,1324],{"id":824,"label":1323},"Engine already knows",{"id":827,"label":1325},"Predictor can estimate",{},{"id":831,"data":1328,"type":572,"tunes":1330},{"text":1329,"level":47},"A simple example: running toward a door",{},{"id":836,"data":1332,"type":544,"tunes":1334},{"text":1333},"Imagine a player sprinting down a corridor toward a door that opens into a dense market square. A conventional streaming system already knows the next zone and can begin loading it. Predictive memory adds a second layer: based on camera direction, movement, prior player choices and current mission state, it can rank which subset of textures, NPC animation data, shaders and geometry is most likely to be needed first.",{},{"id":841,"data":1336,"type":572,"tunes":1338},{"text":1337,"level":47},"Why this could matter more as neural rendering grows",{},{"id":846,"data":1340,"type":544,"tunes":1342},{"text":1341},"Neural graphics adds new data classes: model weights, latent or compressed representations, history buffers and intermediate tensors. If those resources compete with textures and geometry, residency becomes more complicated. Predictive scheduling could decide not only which asset to move, but which representation to keep: raw data, a compressed neural representation, or a reconstructable form.",{},{"id":851,"data":1344,"type":626,"tunes":1349},{"url":1345,"title":1346,"excerpt":1347,"ctaLabel":1348},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 Is Not Just Upscaling: What Generative Neural Rendering Actually Changes","Neural rendering changes what data a frame needs and when it needs it.","Read the neural rendering analysis",{},{"id":859,"data":1351,"type":572,"tunes":1353},{"text":1352,"level":47},"The failure modes are as important as the opportunity",{},{"id":864,"data":1355,"type":544,"tunes":1357},{"text":1356},"Prefetch is not free. A wrong prediction can fetch resources the player never sees, evict useful pages and compete with frame-critical traffic. The correct goal is therefore not maximum prefetch; it is maximum useful hit rate inside a bounded bandwidth and residency budget.",{},{"id":869,"data":1359,"type":609,"tunes":1387},{"content":1360,"stretched":705,"withHeadings":15},[1361,1364,1368,1371,1375,1379,1383],[1306,1362,1363],"What happens","Mitigation idea",[1365,1366,1367],"Bad prediction","Wrong data is prefetched","Confidence thresholds; reserve budget for deterministic demand",[1025,1369,1370],"Useful current data is evicted","Separate predicted and protected residency classes",[1372,1373,1374],"Bandwidth contention","Prefetch steals bandwidth from current frame","Schedule only in headroom windows; enforce per-frame budget",[1376,1377,1378],"Behavior shift","Player turns around or fast-travels","Cheap cancellation and rapid fallback",[1380,1381,1382],"Model overhead","Predictor costs more than it saves","Small models, offline training, or rule-derived lightweight predictors",[1384,1385,1386],"Non-memory stutter","Problem remains","Keep diagnosis separate: shader, CPU, network and storage paths need their own tests",{},{"id":901,"data":1389,"type":572,"tunes":1391},{"text":1390,"level":47},"How you would test it in a real engine",{},{"id":906,"data":1393,"type":659,"tunes":1417},{"steps":1394,"title":1416,"orientation":658},[1395,1398,1401,1404,1407,1410,1413],{"label":1396,"description":1397},"A. Establish baseline","Capture frame-time spikes, page faults\u002Fmigrations, residency misses and data-transfer volume without prediction.",{"label":1399,"description":1400},"B. Add deterministic prefetch only","Measure what conventional engine knowledge already solves.",{"label":1402,"description":1403},"C. Add learned ranking","Use the same memory and bandwidth budget so the comparison is fair.",{"label":1405,"description":1406},"D. Measure hit rate and pollution","Track useful prefetched bytes versus wasted bytes and evictions.",{"label":1408,"description":1409},"E. Measure frame-time distribution","Average FPS is not enough; inspect tail latency and hitch frequency.",{"label":1411,"description":1412},"F. Stress behavior changes","Fast turns, teleport, multiplayer unpredictability and procedural scenes.",{"label":1414,"description":1415},"G. Compare cost","Predictor CPU\u002FGPU time + memory overhead must be lower than the stalls it removes.","Predictive Memory Evaluation",{},{"id":933,"data":1419,"type":572,"tunes":1421},{"text":1420,"level":47},"Where this hypothesis stops",{},{"id":938,"data":1423,"type":544,"tunes":1425},{"text":1424},"Predictive memory cannot make a too-small memory pool infinitely large. It cannot create bandwidth. It cannot fix a shader compilation hitch by guessing the next texture. And it becomes less useful when the player's future is intentionally unpredictable or when the engine already has near-perfect deterministic streaming information.",{},{"id":943,"data":1427,"type":572,"tunes":1429},{"text":1428,"level":47},"A light connection to general AI architecture",{},{"id":948,"data":1431,"type":544,"tunes":1433},{"text":1432},"The deeper pattern is similar to retrieval systems outside gaming: the system should not fetch everything, and it should not wait until the final moment to discover that context is missing. It needs a trigger, a candidate set, a ranking policy and a validity boundary. Gaming simply imposes a much tighter latency budget.",{},{"id":953,"data":1435,"type":626,"tunes":1439},{"url":955,"title":1436,"excerpt":1437,"ctaLabel":1438},"What Is RAG? The Simplest Explanation of How It Works","A general explanation of retrieval, context and why systems fetch information instead of placing everything in the model at once.","Read the RAG explainer on stajic.de",{},{"id":961,"data":1441,"type":572,"tunes":1443},{"text":1442,"level":47},"Primary source",{},{"id":966,"data":1445,"type":973,"tunes":1448},{"link":968,"meta":1446},{"image":1447,"title":971,"description":972},{"url":13},{},{"id":976,"data":1450,"type":973,"tunes":1453},{"link":978,"meta":1451},{"image":1452,"title":981,"description":982},{"url":13},{},{"id":985,"data":1455,"type":1009,"tunes":1462},{"items":1456,"title":1008},[1457,1458,1459,1460,1461],{"id":989,"answer":990,"question":991},{"id":993,"answer":994,"question":995},{"id":997,"answer":998,"question":999},{"id":1001,"answer":1002,"question":1003},{"id":1005,"answer":1006,"question":1007},{},{"id":1012,"data":1464,"type":1032,"tunes":1470},{"title":1014,"entries":1465},[1466,1467,1468,1469],{"term":1017,"anchor":1018,"definition":1019},{"term":1021,"anchor":1022,"definition":1023},{"term":1025,"anchor":1026,"definition":1027},{"term":1029,"anchor":1030,"definition":1031},{},"2.31.6","Game engines already know where the player, camera and world are going. Research shows learned GPU prefetching can predict memory pages. What happens if those ideas are combined?",{"lang":7,"title":534,"content":536,"contentJson":1474,"excerpt":1035},{"time":538,"blocks":1475,"version":1034},[1476,1479,1482,1485,1488,1491,1508,1511,1514,1517,1526,1529,1532,1542,1545,1548,1551,1562,1565,1568,1593,1596,1599,1602,1605,1608,1611,1614,1625,1628,1639,1642,1645,1648,1651,1654,1657,1662,1667,1676],{"id":541,"data":1477,"type":544,"tunes":1478},{"text":543},{},{"id":547,"data":1480,"type":552,"tunes":1481},{"body":549,"title":550,"variant":551},{},{"id":555,"data":1483,"type":552,"tunes":1484},{"body":557,"title":558,"variant":559},{},{"id":562,"data":1486,"type":566,"tunes":1487},{"title":564,"maxLevel":565,"minLevel":47},{},{"id":569,"data":1489,"type":572,"tunes":1490},{"text":571,"level":47},{},{"id":575,"data":1492,"type":617,"tunes":1507},{"rows":1493,"title":608,"layout":609,"columns":1504},[1494,1496,1498,1500,1502],{"id":579,"label":580,"values":1495},{"other":582,"memory":583},{"id":585,"label":586,"values":1497},{"other":588,"memory":589},{"id":591,"label":592,"values":1499},{"other":594,"memory":595},{"id":597,"label":598,"values":1501},{"other":600,"memory":601},{"id":603,"label":604,"values":1503},{"other":606,"memory":607},[1505,1506],{"id":612,"label":613},{"id":615,"label":616},{},{"id":620,"data":1509,"type":626,"tunes":1510},{"url":622,"title":623,"excerpt":624,"ctaLabel":625},{},{"id":629,"data":1512,"type":572,"tunes":1513},{"text":631,"level":47},{},{"id":634,"data":1515,"type":544,"tunes":1516},{"text":636},{},{"id":639,"data":1518,"type":659,"tunes":1525},{"steps":1519,"title":657,"orientation":658},[1520,1521,1522,1523,1524],{"label":643,"description":644},{"label":646,"description":647},{"label":649,"description":650},{"label":652,"description":653},{"label":655,"description":656},{},{"id":662,"data":1527,"type":572,"tunes":1528},{"text":664,"level":47},{},{"id":667,"data":1530,"type":544,"tunes":1531},{"text":669},{},{"id":672,"data":1533,"type":609,"tunes":1541},{"content":1534,"stretched":705,"withHeadings":15},[1535,1536,1537,1538,1539,1540],[676,677,678,679],[681,682,683,684],[686,687,688,689],[691,692,693,694],[696,697,698,699],[701,702,703,704],{},{"id":708,"data":1543,"type":552,"tunes":1544},{"body":710,"title":711,"variant":712},{},{"id":715,"data":1546,"type":544,"tunes":1547},{"text":717},{},{"id":720,"data":1549,"type":572,"tunes":1550},{"text":722,"level":47},{},{"id":725,"data":1552,"type":659,"tunes":1561},{"steps":1553,"title":749,"orientation":658},[1554,1555,1556,1557,1558,1559,1560],{"label":729,"description":730},{"label":732,"description":733},{"label":735,"description":736},{"label":738,"description":739},{"label":741,"description":742},{"label":744,"description":745},{"label":747,"description":748},{},{"id":752,"data":1563,"type":552,"tunes":1564},{"body":754,"title":755,"variant":756},{},{"id":759,"data":1566,"type":572,"tunes":1567},{"text":761,"level":47},{},{"id":764,"data":1569,"type":617,"tunes":1592},{"rows":1570,"title":821,"layout":609,"columns":1589},[1571,1573,1575,1577,1579,1581,1583,1585,1587],{"id":768,"label":769,"values":1572},{"known":771,"learned":772},{"id":774,"label":775,"values":1574},{"known":777,"learned":778},{"id":780,"label":781,"values":1576},{"known":783,"learned":784},{"id":786,"label":787,"values":1578},{"known":789,"learned":790},{"id":792,"label":793,"values":1580},{"known":795,"learned":796},{"id":798,"label":799,"values":1582},{"known":801,"learned":802},{"id":804,"label":805,"values":1584},{"known":807,"learned":808},{"id":810,"label":811,"values":1586},{"known":813,"learned":814},{"id":816,"label":817,"values":1588},{"known":819,"learned":820},[1590,1591],{"id":824,"label":825},{"id":827,"label":828},{},{"id":831,"data":1594,"type":572,"tunes":1595},{"text":833,"level":47},{},{"id":836,"data":1597,"type":544,"tunes":1598},{"text":838},{},{"id":841,"data":1600,"type":572,"tunes":1601},{"text":843,"level":47},{},{"id":846,"data":1603,"type":544,"tunes":1604},{"text":848},{},{"id":851,"data":1606,"type":626,"tunes":1607},{"url":853,"title":854,"excerpt":855,"ctaLabel":856},{},{"id":859,"data":1609,"type":572,"tunes":1610},{"text":861,"level":47},{},{"id":864,"data":1612,"type":544,"tunes":1613},{"text":866},{},{"id":869,"data":1615,"type":609,"tunes":1624},{"content":1616,"stretched":705,"withHeadings":15},[1617,1618,1619,1620,1621,1622,1623],[805,873,874],[876,877,878],[880,881,882],[884,885,886],[888,889,890],[892,893,894],[896,897,898],{},{"id":901,"data":1626,"type":572,"tunes":1627},{"text":903,"level":47},{},{"id":906,"data":1629,"type":659,"tunes":1638},{"steps":1630,"title":930,"orientation":658},[1631,1632,1633,1634,1635,1636,1637],{"label":910,"description":911},{"label":913,"description":914},{"label":916,"description":917},{"label":919,"description":920},{"label":922,"description":923},{"label":925,"description":926},{"label":928,"description":929},{},{"id":933,"data":1640,"type":572,"tunes":1641},{"text":935,"level":47},{},{"id":938,"data":1643,"type":544,"tunes":1644},{"text":940},{},{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erfolgreich abgerufen",{"items":1686,"source":1754,"manualIds":1755,"manualMatchedIds":1756},[1687,1693,1698,1705,1711,1716,1721,1726,1733,1738,1743,1748],{"id":1688,"slug":1689,"title":1690,"excerpt":1691,"featuredImage":14,"publishedAt":1692},"75","gaming-displays-what-actually-changes-feel-latency-refresh-clarity","Gejming ekrani: Šta zapravo menja osećaj (kašnjenje, osvežavanje, jasnoća)","Monitori i televizori nisu samo pikseli. Ekran menja osećaj unosa, glatkoću, jasnoću pokreta i stabilnost. Evo šta je važno, a šta treba zanemariti.","2026-02-19T11:00:00.000Z",{"id":1694,"slug":1695,"title":1696,"excerpt":1697,"featuredImage":14,"publishedAt":1692},"65","gaming-feel-quick-checklist-fix-timing-before-you-upgrade-anything","Brza kontrolna lista za osećaj u igri: Popravite tajming pre nego što bilo šta nadogradite","Brza kontrolna lista za poboljšanje odziva, glatkoće i jasnoće bez kupovine nove opreme. Prvo dijagnostikujte tajming, zatim podesite postavke, pa onda nadogradite.",{"id":1699,"slug":1700,"title":1701,"excerpt":1702,"featuredImage":1703,"publishedAt":1704},"458","the-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system","Game Engine postaje deo renderer, deo scheduler, deo AI sistem","Gejming engine-i koji se koriste za orkestraciju simulacije i renderovanja. Neuralna grafika, prediktivna memorija i gejming agenti dodaju treću odgovornost: odlučivanje gde inferencija pripada unutar frejma i stanja igre.","\u002Fuploads\u002F2026\u002F10\u002Fthe-game-engine-is-becoming-part-renderer-part-scheduler-part-ai-system-1792340569647-63jv23.webp","2026-10-18T12:21:00.000Z",{"id":1706,"slug":1707,"title":1708,"excerpt":1709,"featuredImage":14,"publishedAt":1710},"398","upcoming-nintendo-amiibo-new-figures-release-windows-and-functional-significance","Predstojeći Nintendo amiibo – nove figure, periodi izlaska i funkcionalni značaj","amiibo su fizičke figure opremljene NFC čipovima koje komuniciraju sa Nintendo konzolama. One otključavaju predmete u igri, kozmetičke elemente ili male funkcije igranja u kompatibilnim naslovima. Ovaj pregled kataloga fokusira se na zvanično najavljene predstojeće amiibo figure, njihove očekivane periode izlaska, njihovu funkcionalnu vrednost i njihov položaj unutar šire strukture talasa.","2026-02-27T15:54:00.000Z",{"id":1712,"slug":1713,"title":1714,"excerpt":1715,"featuredImage":14,"publishedAt":1692},"15","fix-bufferbloat-stop-lag-spikes-while-downloading-or-streaming","Popravite Bufferbloat: Zaustavite skokove laga tokom preuzimanja ili strimovanja","Ako ping naglo skoči kada neko preuzima ili strimuje, to je bufferbloat. Ovaj vodič rešava te skokove na nivou rutera.",{"id":283,"slug":1717,"title":1718,"excerpt":1719,"featuredImage":14,"publishedAt":1720},"responsiveness-in-gaming-what-it-really-means","Odzivnost u gejmingu: Šta to zaista znači","Odzivnost je osećaj trenutne kontrole. Evo šta je stvara, šta je uništava i kako je poboljšati bez jurenja mitova.","2026-02-19T12:00:00.000Z",{"id":1722,"slug":1723,"title":1724,"excerpt":1725,"featuredImage":14,"publishedAt":1692},"35","products-objects-that-improve-gaming-feel","Proizvodi: Predmeti koji poboljšavaju osećaj igranja","Odabrani proizvodi za bolju igru: jasnoća, odzivnost, udobnost i stabilnost — birani iskustvom, a ne hajpom.",{"id":1727,"slug":1728,"title":1729,"excerpt":1730,"featuredImage":1731,"publishedAt":1732},"454","why-ai-is-reshaping-gaming-memory-hbm-gddr-vram-and-the-fight-for-capacity","Zašto AI preoblikuje memoriju za igre: HBM, GDDR, VRAM i borba za kapacitet","Memorijski problem koji stoji iza modernog gejminga više se ne svodi samo na to koliko gigabajta je potrebno grafičkoj kartici. AI infrastruktura menja to koje memorijske proizvode dobavljači stavljaju u prvi plan i koliko potrošači mogu da priušte.","\u002Fuploads\u002F2026\u002F10\u002Fwhy-ai-is-reshaping-gaming-memory-hbm-gddr-vram-and-the-fight-for-capacity-1792335495408-ymehaw.webp","2026-10-18T10:56:00.000Z",{"id":1734,"slug":1735,"title":1736,"excerpt":1737,"featuredImage":14,"publishedAt":1692},"53","smoothness-is-frame-pacing-why-stable-frametime-beats-raw-fps","Glatkoća je frame pacing: Zašto stabilan frametime pobeđuje sirovi FPS","Ako vaša igra deluje isprekidano pri visokom FPS-u, to je obično do frame pacing-a. Saznajte šta je frametime, šta uzrokuje skokove i kako ga prvo stabilizovati.",{"id":1739,"slug":1740,"title":1741,"excerpt":1742,"featuredImage":14,"publishedAt":1692},"21","gaming-controls-build-input-feel-without-hand-pain","Gejming kontrole: Izgradite osećaj unosa bez bola u rukama","Kontrole oblikuju odzivnost i udobnost. Ovaj vodič se fokusira na ergonomiju, doslednost i praktične navike podešavanja.",{"id":1744,"slug":1745,"title":1746,"excerpt":1747,"featuredImage":14,"publishedAt":1692},"122","ergonomics-for-long-sessions-comfort-that-prevents-fatigue","Ergonomija za duge sesije: Udobnost koja sprečava zamor","Udobnost je učinak. Koristite ove jednostavne osnove ergonomije da smanjite umor, održite kontrolu doslednom i igrate duže bez bola.",{"id":1749,"slug":1750,"title":1751,"excerpt":1752,"featuredImage":14,"publishedAt":1753},"20","console-vs-pc-display-setup-the-clean-stable-way","Konzola vs PC podešavanje ekrana: Čist, stabilan način","Konzolama i PC-u su potrebni različiti prioriteti prikaza. Ovaj vodič izbegava uobičajene greške i održava kretanje i latenciju doslednim.","2026-02-19T08:00:00.000Z","fallback",[],[]]