{"id":33434,"date":"2019-10-31T21:52:33","date_gmt":"2019-10-31T18:52:33","guid":{"rendered":"https:\/\/prohoster.info\/blog\/kak-rabotaet-szhatie-v-obektno-orientirovannoj-arhitekture-pamyati\/"},"modified":"2019-10-31T21:52:33","modified_gmt":"2019-10-31T18:52:33","slug":"kak-rabotaet-szhatie-v-obektno-orientirovannoj-arhitekture-pamyati","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/administrirovanie\/kak-rabotaet-szhatie-v-obektno-orientirovannoj-arhitekture-pamyati","title":{"rendered":"Kuidas toimib tihendamine objekt-orienteeritud m\u00e4luarhitektuuris","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

MITi insenerite meeskond on t\u00f6\u00f6tanud v\u00e4lja objektip\u00f5hise m\u00e4luhierarhia, et andmetega t\u00f5husamalt t\u00f6\u00f6tada. Artikliks uurime, kuidas see toimib.<\/p>\n

\"Kuidas<\/a><\/noindex><\/a><\/noindex>
\n\/ PxHere<\/noindex> \/ PD<\/i><\/p>\n

Nagu teada, ei kaasne kaasaegsete CPUde tootlikkuse t\u00f5usuga samaaegne m\u00e4lu juurde p\u00e4\u00e4semise viibimise v\u00e4henemine. Aastaste n\u00e4itajate muutuste erinevus v\u00f5ib ulatuda kuni 10 korda (PDF, lk. 3<\/a><\/noindex>). Selle tulemusel tekib kitsaskoht, mis ei v\u00f5imalda t\u00e4iel m\u00e4\u00e4ral olemasolevaid ressursse kasutada ja aeglustab andmete t\u00f6\u00f6tlemist.<\/p>\n

Tootlikkusele avaldab negatiivset m\u00f5ju nn dekompressiooniviivitus. M\u00f5nel juhul v\u00f5ib andmete ettevalmistav dekompressioon v\u00f5tta kuni 64 protsessorits\u00fcklit. <\/p>\n

V\u00f5rdluseks: ujukomaarvude liitmine ja korrutamine<\/a><\/noindex> mitte rohkem kui k\u00fcmme ts\u00fcklit. Probleem seisneb selles, et m\u00e4lu t\u00f6\u00f6tab fikseeritud suurusega andmeplokkidega, samas kui rakendused t\u00f6\u00f6tavad objektidega, mis v\u00f5ivad sisaldada erinevat t\u00fc\u00fcpi andmeid ja erineda suuruse poolest. Probleemi lahendamiseks on MIT-i insenerid v\u00e4lja t\u00f6\u00f6tanud objektorienteeritud m\u00e4luhierarhia, mis optimeerib andmete t\u00f6\u00f6tlemist.<\/p>\n

Kuidas tehnoloogia t\u00f6\u00f6tab<\/h2>\n

\nLahenduse aluseks on kolm tehnoloogiat: Hotpads, Zippads ja COCO tihendamisalgoritm.<\/p>\n

Hotpads on programmide kaudu juhitav \u00fcliv\u00f5imeka registerm\u00e4lu hierarhia (scratchpad<\/a><\/noindex>). Need registreid nimetatakse padjadeks (pads) ja neid on kolm \u2014 L1-st L3-ni. Neis hoitakse erineva suurusega objekte, metaandmeid ja viidete massiive.<\/p>\n

Sisuliselt kujutab arhitektuur endast vahem\u00e4lu s\u00fcsteemi, kuid kohandatud objektidega t\u00f6\u00f6tlemiseks. Padja tase, kus objekt asub, s\u00f5ltub sellest, kui sageli seda kasutatakse. Kui \u00fcks tasanditest \u00ab\u00fcle t\u00e4itub\u00bb, k\u00e4ivitab s\u00fcsteem mehhanismi, mis sarnaneb Java v\u00f5i Go keeles olevate \u00abpr\u00fcgikogujatega\u00bb. See anal\u00fc\u00fcsib, milliseid objekte kasutatakse harvemini kui teisi ja liigub automaatselt nende tasemete vahel.<\/p>\n

Zippads t\u00f6\u00f6tab Hotpads\u2019i alusel \u2014 arhiveerib ja de-arhiveerib andmeid, mis sisenevad v\u00f5i v\u00e4ljuvad kaheastmelise hierarhia viimastest tasemest \u2014 padi L3 ja p\u00f5hism\u00e4lu. Esimeses ja teises padrunis salvestatakse andmed muutumatuna.<\/p><\/blockquote>\n

\n \"Kuidas
\n
\nZippads tihendab objekte, mille maht ei \u00fcleta 128 baiti. Suuremad objektid jagatakse osadeks, mis paigutatakse seej\u00e4rel erinevatesse m\u00e4lukohtadesse. Nagu arendajad kirjutavad, suurendab see l\u00e4henemine efektiivselt kasutatava m\u00e4lu koefitsienti.<\/p>\n

Objektide tihendamiseks kasutatakse algoritmi COCO (Cross-Object COmpression), millest me r\u00e4\u00e4gime hiljem, kuigi s\u00fcsteem suudab t\u00f6\u00f6tada ka Base-Delta-Immediate<\/a><\/noindex> v\u00f5i FPC<\/a><\/noindex>. Algoritm COCO on erinevustihenduse (differential compression<\/a><\/noindex>) t\u00fc\u00fcp. See v\u00f5rreldab objekte \"baasobjektidega\" ja eemaldab korduvad bitid \u2014 vt skeemi allpool:<\/p>\n

\"Kuidas<\/p>\n

MIT inseneride s\u00f5nul on nende objektorienteeritud m\u00e4luhierarhia 17% tootlikum kui traditsioonilised l\u00e4henemisviisid. See on palju l\u00e4hemal kaasaegsete rakenduste arhitektuurile, seega on uuel meetodil potentsiaali.<\/p>\n

Eeldatakse, et esimesena hakkavad tehnoloogiat kasutama ettev\u00f5tted, mis tegelevad suurte andmete ja masin\u00f5ppe algoritmidega. Veel \u00fcks potentsiaalne suund on pilveplatvormid. IaaS-teenusepakkujad saavad v\u00f5imaluse t\u00f5husamalt t\u00f6\u00f6tada virtualiseerimise, andmesalvestuss\u00fcsteemide ja arvutusressurssidega.<\/p>\n

Meie t\u00e4iendavad ressursid ja allikad:<\/b><\/p>\n

\"Kuidas \u201eKuidas me ehitame IaaS\u201d: materjalid 1cloudi t\u00f6\u00f6st<\/a><\/noindex><\/p>\n

\"Kuidas 1cloudi pilvearhitektuuri evolutsioon<\/a><\/noindex>
\n\"Kuidas Objekthoiu teenus 1cloudis<\/a><\/noindex><\/p>\n

\"Kuidas Potentsiaalsed r\u00fcnnakud HTTPS-i vastu ja kaitsemeetodid nende vastu<\/a><\/noindex>
\n\"Kuidas Kuidas erinevad ja sarnanevad Continuous Delivery ja Continuous Integration l\u00e4henemisviisid<\/a><\/noindex>
\n\"Kuidas Kuidas kaitsta teenuseid internetis: 1cloudi kogemus<\/a><\/noindex><\/p><\/blockquote>\n

Allikas: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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