{"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\/pt\/blog\/administrirovanie\/kak-rabotaet-szhatie-v-obektno-orientirovannoj-arhitekture-pamyati","title":{"rendered":"Como funciona a compress\u00e3o na arquitetura de mem\u00f3ria orientada a objetos","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Uma equipe de engenheiros do MIT desenvolveu uma hierarquia de mem\u00f3ria orientada a objetos para um trabalho mais eficiente com dados. Neste artigo, exploramos como ela funciona.<\/p>\n

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

Como \u00e9 sabido, o aumento do desempenho dos CPUs modernos n\u00e3o \u00e9 acompanhado por uma redu\u00e7\u00e3o correspondente na lat\u00eancia de acesso \u00e0 mem\u00f3ria. A diferen\u00e7a nas m\u00e9tricas pode chegar a 10 vezes de um ano para o outro (PDF, p.3<\/a><\/noindex>). Como resultado, um gargalo surge, impedindo o uso pleno dos recursos dispon\u00edveis e desacelerando o processamento de dados.<\/p>\n

A chamada lat\u00eancia de descompress\u00e3o prejudica o desempenho. Em alguns casos, a prepara\u00e7\u00e3o da descompress\u00e3o dos dados pode levar at\u00e9 64 ciclos de processador. <\/p>\n

Para compara\u00e7\u00e3o: a adi\u00e7\u00e3o e multiplica\u00e7\u00e3o de n\u00fameros de ponto flutuante levam<\/a><\/noindex> n\u00e3o mais que dez ciclos. O problema \u00e9 que a mem\u00f3ria trabalha com blocos de dados de tamanho fixo, enquanto os aplicativos operam com objetos que podem conter diferentes tipos de dados e variar em tamanho. Para resolver isso, os engenheiros do MIT desenvolveram uma hierarquia de mem\u00f3ria orientada a objetos que otimiza o processamento de dados.<\/p>\n

Como funciona a tecnologia<\/h2>\n

\nNo cerne da solu\u00e7\u00e3o est\u00e3o tr\u00eas tecnologias: Hotpads, Zippads e o algoritmo de compress\u00e3o COCO.<\/p>\n

Hotpads \u00e9 uma hierarquia de mem\u00f3ria de registradores superr\u00e1pidos controlada por software (scratchpad<\/a><\/noindex>). Esses registradores s\u00e3o chamados de pads e existem tr\u00eas deles \u2014 de L1 a L3. Eles armazenam objetos de diferentes tamanhos, metadados e arrays de ponteiros.<\/p>\n

Essencialmente, a arquitetura \u00e9 um sistema de caches, mas adaptado para trabalhar com objetos. O n\u00edvel do pad onde um objeto est\u00e1 localizado depende de qu\u00e3o frequentemente ele \u00e9 utilizado. Se um dos n\u00edveis \u2018transborda\u2019, o sistema inicia um mecanismo semelhante ao \u2018coletor de lixo\u2019 em linguagens como Java ou Go. Ele analisa quais objetos s\u00e3o usados com menos frequ\u00eancia e os move automaticamente entre os n\u00edveis.<\/p>\n

Zippads opera com base nos Hotpads \u2014 arquivando e desarquivando dados que entram ou saem dos dois \u00faltimos n\u00edveis da hierarquia \u2014 pad L3 e a mem\u00f3ria principal. Nos pads primeiro e segundo, os dados s\u00e3o armazenados inalterados.<\/p><\/blockquote>\n

\n \"Como
\n
\nO Zippads comprime objetos com volume de at\u00e9 128 bytes. Objetos maiores s\u00e3o divididos em partes que s\u00e3o colocadas em diferentes \u00e1reas de mem\u00f3ria. Como afirmam os desenvolvedores, essa abordagem aumenta a efici\u00eancia do uso da mem\u00f3ria.<\/p>\n

Para comprimir objetos, \u00e9 utilizado o algoritmo COCO (Cross-Object COmpression), que ser\u00e1 detalhado a seguir, embora o sistema tamb\u00e9m possa funcionar com Base-Delta-Immediate<\/a><\/noindex> ou FPC<\/a><\/noindex>. O algoritmo COCO representa uma forma de compress\u00e3o diferencial (differential compression<\/a><\/noindex>). Ele compara objetos a um \"base\" e remove bits duplicados \u2014 veja o diagrama abaixo:<\/p>\n

\"Como<\/p>\n

Segundo engenheiros do MIT, sua hierarquia de mem\u00f3ria orientada a objetos \u00e9 17% mais eficiente do que abordagens cl\u00e1ssicas. Ela se aproxima mais da arquitetura de aplica\u00e7\u00f5es modernas, portanto, o novo m\u00e9todo possui potencial.<\/p>\n

Espera-se que, em primeiro lugar, a tecnologia comece a ser utilizada por empresas que trabalham com grandes dados e algoritmos de aprendizado de m\u00e1quina. Outro potencial a ser explorado s\u00e3o as plataformas em nuvem. Provedores de IaaS ter\u00e3o a capacidade de operar de forma mais eficiente com virtualiza\u00e7\u00e3o, sistemas de armazenamento e recursos computacionais.<\/p>\n

Nossos recursos e fontes adicionais:<\/b><\/p>\n

\"Como \u00abComo constru\u00edmos IaaS\u00bb: materiais sobre o trabalho da 1cloud<\/a><\/noindex><\/p>\n

\"Como A evolu\u00e7\u00e3o da arquitetura em nuvem da 1cloud<\/a><\/noindex>
\n\"Como Servi\u00e7o de armazenamento de objetos na 1cloud<\/a><\/noindex><\/p>\n

\"Como Ataques potenciais ao HTTPS e formas de prote\u00e7\u00e3o contra eles<\/a><\/noindex>
\n\"Como Semelhan\u00e7as e diferen\u00e7as entre as abordagens de Continuous Delivery e Continuous Integration<\/a><\/noindex>
\n\"Como Como proteger um servidor na internet: a experi\u00eancia da 1cloud<\/a><\/noindex><\/p><\/blockquote>\n

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

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