{"id":2337,"date":"2025-09-30T11:39:52","date_gmt":"2025-09-30T18:39:52","guid":{"rendered":"https:\/\/sycomp.com\/?p=2337"},"modified":"2025-09-30T11:39:52","modified_gmt":"2025-09-30T18:39:52","slug":"obtencao-de-97-de-utilizacao-da-gpu-no-google-cloud-com-a-plataforma-inteligente-de-armazenamento-de-dados-sycomp","status":"publish","type":"post","link":"https:\/\/sycomp.com\/pt\/recurso\/obtencao-de-97-de-utilizacao-da-gpu-no-google-cloud-com-a-plataforma-inteligente-de-armazenamento-de-dados-sycomp\/","title":{"rendered":"Obten\u00e7\u00e3o da utiliza\u00e7\u00e3o de GPU 97% no Google Cloud com a plataforma de armazenamento inteligente de dados Sycomp"},"content":{"rendered":"<div id=\"cs-content\" class=\"cs-content\"><div class=\"x-section e2337-e1 m1sx-0\"><div class=\"x-row e2337-e2 m1sx-1 m1sx-2\"><div class=\"x-row-inner\"><div class=\"x-col e2337-e3 m1sx-3\"><div class=\"x-text x-content e2337-e4 m1sx-4 resource-text use-as-excerpt\"><h2>Introdu\u00e7\u00e3o<\/h2>\n<p>Como arquiteto de HPC, sei que as GPUs s\u00e3o um recurso valioso - altamente especializado, imensamente poderoso e capaz de realizar feitos extraordin\u00e1rios. Tamb\u00e9m sei que as GPUs n\u00e3o existem em uma bolha, elas s\u00e3o t\u00e3o eficientes quanto os sistemas que as suportam. Por exemplo, se suas GPUs n\u00e3o conseguem acessar os dados com rapidez suficiente, elas ficam ociosas, desperdi\u00e7ando tempo, computa\u00e7\u00e3o, investimento e dinheiro.<\/p>\n<p>Esse foi o desafio apresentado por um l\u00edder global de P&amp;D do AI.  Eles nos pediram para provar que, no cloud, poder\u00edamos manter suas GPUs totalmente abastecidas com dados sob as condi\u00e7\u00f5es mais exigentes.<\/p>\n\n<h2>O desafio<\/h2>\n<p>N\u00e3o se tratava de um exerc\u00edcio te\u00f3rico. O cliente exigiu a valida\u00e7\u00e3o do MLPerf Storage v1.0, a refer\u00eancia padr\u00e3o do setor para o desempenho do armazenamento AI\/ML. Seus crit\u00e9rios inclu\u00edam:<\/p>\n\n<ul>\n \t<li>Taxa de transfer\u00eancia sustentada de \u226524 GiB\/s por n\u00f3<\/li>\n \t<li>Resultado bem-sucedido de \"train_au_meet_expectation: success\" usando o conjunto de dados Unet3D<\/li>\n \t<li>Alto IOPS e taxa de transfer\u00eancia sequencial para treinamento e infer\u00eancia<\/li>\n \t<li>Escalabilidade futura em clusters de treinamento com v\u00e1rios n\u00f3s<\/li>\n \t<li>Opera\u00e7\u00e3o no Google Cloud, apesar da aus\u00eancia de suporte nativo ao GPU Direct<\/li>\n<\/ul>\n<p>Em resumo, precis\u00e1vamos oferecer desempenho de classe de supercomputador em um ambiente p\u00fablico cloud.<\/p>\n\n<h2>A solu\u00e7\u00e3o Sycomp<\/h2>\n<p>Para enfrentar esse desafio, implantamos o <a href=\"https:\/\/sycomp.com\/pt\/solucao\/hpc\/armazenamento\/\" target=\"_blank\" rel=\"noopener\">Plataforma de armazenamento inteligente de dados Sycomp<\/a> no Google Cloud, alimentado pelo IBM Storage Scale. A arquitetura inclu\u00eda:<\/p>\n\n<ul>\n \t<li>Computa\u00e7\u00e3o: Uma \u00fanica inst\u00e2ncia do A3 Ultra com GPUs de classe H100<\/li>\n \t<li>Armazenamento: Cluster C3 de cinco n\u00f3s com volumes Hyperdisk Balanced<\/li>\n \t<li>Orquestra\u00e7\u00e3o de software: IBM Storage Scale<\/li>\n<\/ul>\n<p>Essa configura\u00e7\u00e3o foi projetada para garantir que as GPUs pudessem operar com todo o potencial sem esperar pelo fornecimento de dados.<\/p>\n\n<h2>Resultados de benchmarking<\/h2>\n<p>O benchmark MLPerf Storage v1.0 foi executado em um \u00fanico n\u00f3 de computa\u00e7\u00e3o A3 Ultra. O sistema alcan\u00e7ou:<\/p>\n\n<ul>\n \t<li>Taxa de transfer\u00eancia sustentada de 23 GiB\/s<\/li>\n \t<li>\"train_au_meet_expectation: success\" com o conjunto de dados Unet3D<\/li>\n \t<li>157.000 IOPS em leituras aleat\u00f3rias de 4KiB<\/li>\n \t<li>28,5 GiB\/s em leituras sequenciais de 64 KiB<\/li>\n \t<li>Utiliza\u00e7\u00e3o da GPU 97% - provando que o armazenamento n\u00e3o era mais o gargalo<\/li>\n<\/ul>\n<h2>Por que \u00e9 importante<\/h2>\n<p>Para o cliente, n\u00e3o se tratava apenas de atingir os n\u00fameros de refer\u00eancia - tratava-se de confian\u00e7a. Confian\u00e7a de que sua infraestrutura poderia suportar cargas de trabalho ambiciosas de AI sem comprometimento. Com a valida\u00e7\u00e3o do MLPerf, eles podem avan\u00e7ar com pipelines de treinamento no Google Cloud, com a garantia de desempenho e escalabilidade.<\/p>\n<p>Para os arquitetos do HPC, a conclus\u00e3o foi clara: os ambientes cloud podem atingir o desempenho de classe HPC quando projetados corretamente. O armazenamento e a movimenta\u00e7\u00e3o de dados s\u00e3o t\u00e3o importantes quanto as pr\u00f3prias GPUs. Ao projetar um sistema equilibrado, permitimos que os pesquisadores se concentrassem na inova\u00e7\u00e3o, n\u00e3o na infraestrutura.<\/p>\n\n<h2>Li\u00e7\u00f5es aprendidas<\/h2>\n<ul>\n \t<li>Os pipelines de dados s\u00e3o essenciais: As GPUs s\u00e3o t\u00e3o r\u00e1pidas quanto os dados fornecidos a elas.<\/li>\n \t<li>O Cloud est\u00e1 pronto para o HPC: com o design correto, \u00e9 poss\u00edvel obter desempenho e escalabilidade.<\/li>\n \t<li>O benchmarking valida os resultados: O MLPerf forneceu uma estrutura transparente e confi\u00e1vel para comprovar a capacidade do mundo real.<\/li>\n<\/ul>\n<p>No final das contas, o objetivo n\u00e3o era simplesmente atingir a utiliza\u00e7\u00e3o da GPU 97% - era capacitar uma equipe de pesquisa de classe mundial a confiar em sua infraestrutura e acelerar suas descobertas. Quando as GPUs permanecem totalmente abastecidas, a inova\u00e7\u00e3o acontece a toda velocidade.<\/p>\n\n<h2>Saiba mais<\/h2>\n<p><strong>Explorar a metodologia completa de benchmarking, a arquitetura e o processo de valida\u00e7\u00e3o de desempenho, <a href=\"https:\/\/sycomp.com\/pt\/recurso\/benchmarking-de-armazenamento-mlperf-e-fio-com-a-plataforma-inteligente-de-armazenamento-de-dados-sycomp\/\" target=\"_blank\" rel=\"noopener\">Leia o resumo t\u00e9cnico do Sycomp sobre o benchmarking de armazenamento MLPerf<\/a><\/strong>.<\/p>\n<p>Ele fornece insights detalhados sobre como a <a href=\"https:\/\/sycomp.com\/pt\/solucao\/hpc\/armazenamento\/\" target=\"_blank\" rel=\"noopener\">Plataforma de armazenamento inteligente de dados Sycomp<\/a> oferece desempenho de classe HPC no cloud.<\/p><\/div><\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>Como arquiteto de HPC, sei que as GPUs s\u00e3o um recurso valioso - altamente especializado, imensamente poderoso e capaz de realizar feitos extraordin\u00e1rios. Tamb\u00e9m sei que as GPUs n\u00e3o existem em uma bolha, elas s\u00e3o t\u00e3o eficientes quanto os sistemas que as suportam. Por exemplo, se suas GPUs n\u00e3o conseguem acessar os dados com rapidez suficiente, elas ficam ociosas, desperdi\u00e7ando tempo, computa\u00e7\u00e3o, investimento e dinheiro....<\/p>","protected":false},"author":3,"featured_media":2338,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","rs_blank_template":"","rs_page_bg_color":"","slide_template_v7":"","footnotes":""},"categories":[1],"tags":[],"resource-type":[16],"class_list":["post-2337","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","resource-type-blog"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Achieving 97% GPU Utilization on Google Cloud with Sycomp Intelligent Data Storage Platform - Sycomp Inc<\/title>\n<meta name=\"description\" content=\"GPUs don\u2019t exist in a bubble: they\u2019re only as effective as the systems that support them. 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