{"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\/da\/blog\/administrirovanie\/kak-rabotaet-szhatie-v-obektno-orientirovannoj-arhitekture-pamyati","title":{"rendered":"Hvordan fungerer komprimering i en objektorienteret hukommelsesarkitektur","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Et team af ingeni\u00f8rer fra MIT har udviklet en objektorienteret hukommelsesstruktur for mere effektiv h\u00e5ndtering af data. I denne artikel unders\u00f8ger vi, hvordan den er opbygget.<\/p>\n

\"Hvordan<\/a><\/noindex><\/a><\/noindex>
\n\/ Hvad f\u00e5r I ud af det<\/noindex> \/ PD<\/i><\/p>\n

Som bekendt stiger ydeevnen for moderne CPU'er ikke ledsaget af et tilsvarende fald i latenstiden ved adgang til hukommelse. Forskellen i \u00e6ndringerne af m\u00e5lingerne fra \u00e5r til \u00e5r kan n\u00e5 op til 10 gange (PDF, s.3<\/a><\/noindex>). Resultatet bliver en flaskehals, der forhindrer fuld udnyttelse af de tilg\u00e6ngelige ressourcer og bremser databehandlingen.<\/p>\n

Den s\u00e5kaldte dekompressionslatenstid skader ydeevnen. I nogle tilf\u00e6lde kan der g\u00e5 op til 64 processorcykler til forberedende dekompression af data. <\/p>\n

Til sammenligning tager addition og multiplikation af flydende tal h\u00f8jst<\/a><\/noindex> ti cykler. Problemet er, at hukommelsen arbejder med blokke af data i fast st\u00f8rrelse, mens applikationer arbejder med objekter, der kan indeholde forskellige datatyper og adskille sig i st\u00f8rrelse. For at l\u00f8se problemet har ingeni\u00f8rer fra MIT udviklet en objektorienteret hukommelsesstruktur, der optimerer databehandling.<\/p>\n

Hvordan teknologien fungerer<\/h2>\n

\nL\u00f8sningen bygger p\u00e5 tre teknologier: Hotpads, Zippads og COCO komprimeringsalgoritmen.<\/p>\n

Hotpads er en softwarestyret struktur for superhurtig registerhukommelse (scratchpad<\/a><\/noindex>). Disse registre kaldes pads, og der er tre af dem \u2014 fra L1 til L3. De rummer objekter af forskellige st\u00f8rrelser, metadata og arrays af pegepunkter.<\/p>\n

I sin essens udg\u00f8r arkitekturen et cachesystem, men designet til arbejde med objekter. Niveauet af det pad, hvor objektet befinder sig, afh\u00e6nger af, hvor ofte det bruges. Hvis et af niveauerne 'overfyldes', aktiverer systemet en mekanisme, der ligner 'garbage collectors' i programmeringssprogene Java eller Go. Den analyserer, hvilke objekter der bruges sj\u00e6ldnere end de andre og flytter dem automatisk mellem niveauerne.<\/p>\n

Zippads arbejder p\u00e5 grundlag af Hotpads \u2014 arkiverer og dekomprimerer data, der kommer til eller forlader de to sidste niveauer i strukturen \u2014 pad L3 og hovedhukommelsen. I f\u00f8rste og andet pad gemmes dataene u\u00e6ndret.<\/p><\/blockquote>\n

\n \"Hvordan
\n
\nZippads komprimerer objekter, hvis st\u00f8rrelse ikke overstiger 128 byte. St\u00f8rre objekter opdeles i dele, som derefter placeres i forskellige omr\u00e5der af hukommelsen. Som udviklerne skriver, \u00f8ger denne tilgang den effektivt anvendte hukommelse.<\/p>\n

Til komprimering af objekter anvendes algoritmen COCO (Cross-Object COmpression), som vi vil beskrive n\u00e6rmere. Systemet kan dog ogs\u00e5 arbejde med Base-Delta-Immediate<\/a><\/noindex> eller FPC<\/a><\/noindex>. COCO-algoritmen er en form for differenskomprimering (differential compression<\/a><\/noindex>). Den sammenligner objekter med 'basis'-objekter og fjerner gentagne bits \u2014 se diagrammet nedenfor:<\/p>\n

\"Hvordan<\/p>\n

If\u00f8lge ingeni\u00f8rer fra MIT er deres objektorienterede hukommelsesstruktur 17 % mere effektiv end klassiske tilgange. Den ligner meget mere de moderne applikationers arkitektur, s\u00e5 den nye metode har potentiale.<\/p>\n

Det forventes, at teknologien i f\u00f8rste omgang kan blive anvendt af virksomheder, der arbejder med store datam\u00e6ngder og maskinl\u00e6ringsalgoritmer. Et andet potentielt omr\u00e5de er cloud-platforme. IaaS-udbydere vil f\u00e5 mulighed for at arbejde mere effektivt med virtualisering, datalagringssystemer og beregningsressourcer.<\/p>\n

Vore ekstra ressourcer og kilder:<\/b><\/p>\n

\"Hvordan \u00abHvordan vi bygger IaaS\u00bb: materiale om arbejdet med 1cloud<\/a><\/noindex><\/p>\n

\"Hvordan Evolutionsarkitektur af 1cloud<\/a><\/noindex>
\n\"Hvordan Objekth\u00e5ndteringstjenesten i 1cloud<\/a><\/noindex><\/p>\n

\"Hvordan Potentielle angreb p\u00e5 HTTPS og hvordan man beskytter sig imod dem<\/a><\/noindex>
\n\"Hvordan Hvordan Continuous Delivery og Continuous Integration ligner hinanden og adskiller sig<\/a><\/noindex>
\n\"Hvordan Hvordan man beskytter serveren p\u00e5 internettet: erfaring fra 1cloud<\/a><\/noindex><\/p><\/blockquote>\n

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

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