{"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":"97-de-utilizacion-de-la-gpu-en-google-cloud-con-la-plataforma-inteligente-de-almacenamiento-de-datos-sycomp","status":"publish","type":"post","link":"https:\/\/sycomp.com\/es\/recurso\/97-de-utilizacion-de-la-gpu-en-google-cloud-con-la-plataforma-inteligente-de-almacenamiento-de-datos-sycomp\/","title":{"rendered":"Utilizaci\u00f3n de la GPU 97% en Google Cloud con la plataforma de almacenamiento inteligente de datos 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>Introducci\u00f3n<\/h2>\n<p>Como arquitecto de HPC, s\u00e9 que las GPU son un recurso valioso: altamente especializado, inmensamente potente y capaz de haza\u00f1as extraordinarias. Tambi\u00e9n s\u00e9 que las GPU no existen en una burbuja, s\u00f3lo son tan eficaces como los sistemas que las soportan. Por ejemplo, si las GPU no pueden acceder a los datos con la suficiente rapidez, se quedan inactivas y pierden tiempo, c\u00e1lculo, inversi\u00f3n y dinero.<\/p>\n<p>Ese fue el reto que nos plante\u00f3 un l\u00edder mundial en I+D de AI.  Nos pidieron que demostr\u00e1ramos que en el cloud pod\u00edamos mantener sus GPU totalmente abastecidas de datos en las condiciones m\u00e1s exigentes.<\/p>\n\n<h2>El desaf\u00edo<\/h2>\n<p>No se trataba de un ejercicio te\u00f3rico. El cliente exig\u00eda una validaci\u00f3n con MLPerf Storage v1.0, la referencia est\u00e1ndar del sector para el rendimiento del almacenamiento AI\/ML. Sus criterios inclu\u00edan:<\/p>\n\n<ul>\n \t<li>Rendimiento sostenido \u226524 GiB\/s por nodo<\/li>\n \t<li>Resultado satisfactorio de \"train_au_meet_expectation: success\" con el conjunto de datos Unet3D<\/li>\n \t<li>Alto rendimiento secuencial y de IOPS tanto para la formaci\u00f3n como para la inferencia<\/li>\n \t<li>Futura escalabilidad en cl\u00fasteres de formaci\u00f3n multinodo<\/li>\n \t<li>Funcionamiento en Google Cloud, a pesar de la ausencia de soporte nativo GPU Direct<\/li>\n<\/ul>\n<p>En resumen, necesit\u00e1bamos ofrecer un rendimiento de superordenador en un entorno cloud p\u00fablico.<\/p>\n\n<h2>La soluci\u00f3n Sycomp<\/h2>\n<p>Para hacer frente a este reto, desplegamos el <a href=\"https:\/\/sycomp.com\/es\/solucion\/hpc\/almacenamiento\/\" target=\"_blank\" rel=\"noopener\">Sycomp Plataforma inteligente de almacenamiento de datos<\/a> en Google Cloud, con tecnolog\u00eda IBM Storage Scale. La arquitectura inclu\u00eda:<\/p>\n\n<ul>\n \t<li>Computaci\u00f3n: Una \u00fanica instancia A3 Ultra con GPU de clase H100<\/li>\n \t<li>Almacenamiento: Cl\u00faster C3 de cinco nodos con vol\u00famenes Hyperdisk Balanced.<\/li>\n \t<li>Orquestaci\u00f3n de software: IBM Storage Scale<\/li>\n<\/ul>\n<p>Esta configuraci\u00f3n se dise\u00f1\u00f3 para garantizar que las GPU pudieran funcionar a pleno rendimiento sin tener que esperar a la entrega de datos.<\/p>\n\n<h2>Resultados de la evaluaci\u00f3n comparativa<\/h2>\n<p>La prueba MLPerf Storage v1.0 se ejecut\u00f3 en un \u00fanico nodo inform\u00e1tico A3 Ultra. El sistema logr\u00f3:<\/p>\n\n<ul>\n \t<li>Rendimiento sostenido de 23 GiB\/s<\/li>\n \t<li>\"train_au_meet_expectation: \u00e9xito\" con el conjunto de datos Unet3D<\/li>\n \t<li>157.000 IOPS en lecturas aleatorias de 4 KB<\/li>\n \t<li>28,5 GiB\/s en lecturas secuenciales de 64 KB<\/li>\n \t<li>Utilizaci\u00f3n de la GPU 97%, lo que demuestra que el almacenamiento ya no era el cuello de botella.<\/li>\n<\/ul>\n<h2>Por qu\u00e9 es importante<\/h2>\n<p>Para el cliente, no se trataba s\u00f3lo de alcanzar las cifras de referencia, sino de confianza. Confianza en que su infraestructura pod\u00eda soportar las ambiciosas cargas de trabajo de AI sin problemas. Con la validaci\u00f3n de MLPerf, pueden seguir adelante con los canales de formaci\u00f3n en Google Cloud, con la garant\u00eda de rendimiento y escalabilidad.<\/p>\n<p>Para los arquitectos de HPC, la conclusi\u00f3n era clara: los entornos cloud pueden alcanzar un rendimiento de clase HPC si se dise\u00f1an correctamente. El almacenamiento y el movimiento de datos son tan importantes como las propias GPU. Al dise\u00f1ar un sistema equilibrado, permitimos a los investigadores centrarse en la innovaci\u00f3n, no en la infraestructura\".<\/p>\n\n<h2>Lecciones aprendidas<\/h2>\n<ul>\n \t<li>Las canalizaciones de datos son fundamentales: La velocidad de las GPU depende de los datos que reciban.<\/li>\n \t<li>Cloud est\u00e1 listo para HPC: con el dise\u00f1o adecuado, el rendimiento y la escalabilidad son alcanzables.<\/li>\n \t<li>La evaluaci\u00f3n comparativa valida los resultados: MLPerf proporcion\u00f3 un marco transparente y fiable para demostrar la capacidad en el mundo real.<\/li>\n<\/ul>\n<p>Al fin y al cabo, el objetivo no era simplemente conseguir una utilizaci\u00f3n de la GPU 97%, sino dar a un equipo de investigaci\u00f3n de talla mundial la posibilidad de confiar en su infraestructura y acelerar sus descubrimientos. Cuando las GPU est\u00e1n totalmente abastecidas, la innovaci\u00f3n se produce a toda velocidad.<\/p>\n\n<h2>M\u00e1s informaci\u00f3n<\/h2>\n<p><strong>Explorar toda la metodolog\u00eda de evaluaci\u00f3n comparativa, la arquitectura y el proceso de validaci\u00f3n del rendimiento, <a href=\"https:\/\/sycomp.com\/es\/recurso\/evaluacion-comparativa-de-almacenamiento-mlperf-y-fio-con-la-plataforma-inteligente-de-almacenamiento-de-datos-sycomp\/\" target=\"_blank\" rel=\"noopener\">lea el informe t\u00e9cnico de Sycomp sobre la evaluaci\u00f3n comparativa MLPerf Storage<\/a><\/strong>.<\/p>\n<p>Ofrece una visi\u00f3n en profundidad de c\u00f3mo la <a href=\"https:\/\/sycomp.com\/es\/solucion\/hpc\/almacenamiento\/\" target=\"_blank\" rel=\"noopener\">Sycomp Plataforma inteligente de almacenamiento de datos<\/a> ofrece un rendimiento de clase HPC en el cloud.<\/p><\/div><\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>Como arquitecto de HPC, s\u00e9 que las GPU son un recurso valioso: altamente especializado, inmensamente potente y capaz de haza\u00f1as extraordinarias. Tambi\u00e9n s\u00e9 que las GPU no existen en una burbuja, s\u00f3lo son tan eficaces como los sistemas que las soportan. Por ejemplo, si las GPU no pueden acceder a los datos con la suficiente rapidez, se quedan inactivas, malgastando tiempo, c\u00e1lculo e inversi\u00f3n y dinero.....<\/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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