{"id":166101,"date":"2025-06-23T12:38:58","date_gmt":"2025-06-23T19:38:58","guid":{"rendered":"https:\/\/blog.everpuredata.com\/?p=166101"},"modified":"2025-11-10T12:54:55","modified_gmt":"2025-11-10T20:54:55","slug":"pure-storage-optimizing-gpu-performance-utilization-for-ai-workloads","status":"publish","type":"post","link":"https:\/\/blog.everpuredata.com\/es\/purely-technical\/pure-storage-optimizing-gpu-performance-utilization-for-ai-workloads\/","title":{"rendered":"C\u00f3mo elimina Pure Storage los cuellos de botella computacionales, optimizando el uso de la GPU para las cargas de trabajo de la IA"},"content":{"rendered":"\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column has-medium-font-size is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:70%\">\n<div class=\"wp-block-group has-border-color has-pure-orange-100-border-color has-primary-background-color has-background is-layout-flow wp-block-group-is-layout-flow\" style=\"border-width:2px;border-top-left-radius:16px;border-top-right-radius:16px;border-bottom-left-radius:16px;border-bottom-right-radius:16px;padding-top:30px;padding-right:30px;padding-bottom:30px;padding-left:30px\">\n<h3 class=\"wp-block-heading\" id=\"h-summary\">Resumen<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">La plataforma Pure Storage aborda los retos t\u00e9cnicos de las cargas de trabajo de IA modernas, lo que permite que las organizaciones maximicen el potencial de su infraestructura de IA.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-group pdf-print-hide is-content-justification-right is-nowrap is-layout-flex wp-container-core-group-is-layout-f726d978 wp-block-group-is-layout-flex\"><div class=\"pdfprnt-buttons\"><a href=\"https:\/\/blog.everpuredata.com\/es\/wp-json\/wp\/v2\/posts\/166101?print=pdf\" class=\"pdfprnt-button pdfprnt-button-pdf\" target=\"_blank\" ><img decoding=\"async\" src=\"https:\/\/blog.everpuredata.com\/wp-content\/plugins\/pdf-print-pro\/images\/pdf.png?1953174090\" alt=\"image_pdf\" title=\"Ver PDF\" \/><\/a><a href=\"https:\/\/blog.everpuredata.com\/es\/wp-json\/wp\/v2\/posts\/166101?print=print\" class=\"pdfprnt-button pdfprnt-button-print\" target=\"_blank\" ><img decoding=\"async\" src=\"https:\/\/blog.everpuredata.com\/wp-content\/plugins\/pdf-print-pro\/images\/print.png?245231721\" alt=\"image_print\" title=\"Imprimir contenido\" \/><\/a><\/div>\n<\/div>\n\n\n\n<div id=\"CONTENT\" class=\"wp-block-group is-layout-flow wp-block-group-is-layout-flow\">\n<p class=\"wp-block-paragraph\">Imagine que una empresa acaba de realizar una inversi\u00f3n de 100 000 d\u00f3lares \u2014o incluso de 1 mill\u00f3n de d\u00f3lares\u2014 en un cl\u00faster de GPU para la IA, pero solo el 62 % de esas GPU se utilizan de manera constante a capacidad. Eso podr\u00eda generar un importante despilfarro financiero y una p\u00e9rdida de ROI.\u00a0<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Sin embargo, los propietarios de infraestructuras pueden tomar una decisi\u00f3n cr\u00edtica para prevenir estas p\u00e9rdidas \u2014 no solo las p\u00e9rdidas financieras, sino tambi\u00e9n las p\u00e9rdidas de rendimiento, eficiencia y oportunidad. Comienza por ver una infraestructura de almacenamiento de datos con un rendimiento insuficiente, lo que puede afectar en gran medida al rendimiento de la GPU y desperdiciar ciclos de la GPU.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">En los entornos de IA, maximizar el uso de la GPU es crucial para unas operaciones eficientes. Pure Storage aborda estos retos proporcionando arquitecturas de almacenamiento dise\u00f1adas para optimizar el uso de la GPU. Veamos c\u00f3mo.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-technical-constraints-and-solutions\"><strong>Restricciones y soluciones t\u00e9cnicas<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">La <a href=\"https:\/\/www.purestorage.com\/pure-advantage\/platform.html\" target=\"_blank\" rel=\"noreferrer noopener\">plataforma de Pure Storage <\/a> aborda tres limitaciones t\u00e9cnicas clave:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Latencia de la ingesta de datos:<\/strong> Reducir los tiempos de espera I\/O para garantizar un flujo de datos continuo<\/li>\n\n\n\n<li><strong>L\u00edmites de concurrencia:<\/strong> Mejora de las capacidades de entrenamiento de varias GPU <\/li>\n\n\n\n<li><strong>Variabilidad del rendimiento: <\/strong>Gesti\u00f3n de las r\u00e1fagas de inferencia para un rendimiento constante<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-gpu-storage-interdependence-in-ai-pipelines\"><strong>Interdependencia del almacenamiento en la GPU en los pipelines de IA <\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Las cargas de trabajo de IA modernas requieren una entrega de datos en paralelo que coincida con el ancho de banda de la memoria de la GPU. Por ejemplo, las GPU NVIDIA Blackwell exigen un ancho de banda de memoria agregado alto. <a href=\"https:\/\/www.purestorage.com\/products\/unstructured-data-storage\/flashblade-s.html\" target=\"_blank\" rel=\"noreferrer noopener\">FlashBlade\/\/S<\/a>\u2122 de Pure Storage<sup>\u00ae<\/sup> proporciona un alto rendimiento gracias a:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Optimizaci\u00f3n del protocolo NVMe-oF:<\/strong> Mejorar la eficiencia de la transferencia de datos<\/li>\n\n\n\n<li><strong>M\u00f3dulos DirectFlash\u00ae basados en ARM<\/strong>:<strong><sup><\/sup><\/strong><strong> <\/strong> Reducir la sobrecarga de la pila de software<\/li>\n\n\n\n<li><strong>Ajuste de paridad din\u00e1mica:<\/strong> Optimizaci\u00f3n de las cargas de trabajo mixtas de lectura\/escritura<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Esta arquitectura reduce significativamente los ciclos de interrupci\u00f3n de los datos, manteniendo saturados los n\u00facleos tensores de la GPU.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-technical-benchmark-storage-impact-on-training-efficiency\"><strong>Referencia t\u00e9cnica: Impacto del almacenamiento en la eficiencia del entrenamiento<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Sistema m\u00e9trico<\/strong><\/td><td><strong>Almacenamiento en HDD tradicional<\/strong><\/td><td><strong>Soluciones All-flash de Pure Storage <\/strong><\/td><td><strong>Impacto en la formaci\u00f3n<\/strong><\/td><\/tr><tr><td>Hora de \u00e9poca<\/td><td>3-5 veces m\u00e1s larga<\/td><td>L\u00ednea de base (1 vez)<\/td><td>El almacenamiento flash puede reducir el tiempo de entrenamiento en un 50-70% comparado con los discos duros.<\/td><\/tr><tr><td>Utilizaci\u00f3n de GPU <\/td><td>30-60%<\/td><td>85-98%<\/td><td>Un mayor uso significa que las GPU pasan menos tiempo esperando los datos<\/td><\/tr><tr><td>Eficiencia energ\u00e9tica (FLOPS\/vatio)<\/td><td>M\u00e1s bajo<\/td><td>2-3 veces m\u00e1s alto<\/td><td>Las soluciones All-flash permiten un mayor c\u00e1lculo por vatio de potencia.<\/td><\/tr><tr><td>Latencia de lectura<\/td><td>5-10 ms<\/td><td>0,2-1 ms<\/td><td>Una latencia m\u00e1s baja garantiza que las GPU alimentan los datos r\u00e1pidamente<\/td><\/tr><tr><td>Throughput<\/td><td>100-200 MB\/s por unidad<\/td><td>5-20 GB\/s<\/td><td>Un mayor caudal evita la escasez de datos<\/td><\/tr><tr><td>IOPS<\/td><td>100-200 por unidad<\/td><td>M\u00e1s de 100 000<\/td><td>Crucial para patrones de acceso aleatorio en grandes conjuntos de datos<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-solving-next-gen-ai-workload-challenges\"><strong>Resolver los retos de la carga de trabajo de la IA de \u00faltima generaci\u00f3n<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">En cuanto al uso de la GPU, la plataforma Pure Storage ofrece:<\/p>\n\n\n\n<h4 class=\"wp-block-heading has-secondary-color has-text-color has-link-color wp-elements-1e92795056151d8358e7a762f9aa8f1b\" id=\"h-retrieval-augmented-generation-rag-optimization\"><strong>Optimizaci\u00f3n de la generaci\u00f3n aumentada de recuperaci\u00f3n (RAG)<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Una soluci\u00f3n RAG conjunta de Pure Storage y NVIDIA incluye:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Almacenamiento directo de GPU: <\/strong>Evitar los cuellos de botella de la CPU<\/li>\n\n\n\n<li><strong>Canalizaciones indexadas por Metadata: <\/strong>Reducci\u00f3n de la latencia r\u00e1pida del LLM<\/li>\n\n\n\n<li><strong>Rendimiento controlado por QoS: <\/strong>Garantizar un rendimiento sostenido<\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\">Obtenga <a href=\"https:\/\/blog.everpuredata.com\/perspectives\/optimize-genai-apps-with-rag-from-pure-storage-and-nvidia\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em>m\u00e1s informaci\u00f3n sobre la soluci\u00f3n RAG<\/em><\/a><em>.<\/em><\/p>\n\n\n\n<h4 class=\"wp-block-heading has-secondary-color has-text-color has-link-color wp-elements-451cb3195c4c95ac321e7742b6366bca\" id=\"h-energy-efficient-scaling\"><strong>Escalamiento energ\u00e9ticamente eficiente<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Compresi\u00f3n acelerada por hardware: <\/strong>Reducir la huella de datos<\/li>\n\n\n\n<li><strong>Niveles predictivos: <\/strong>Trasladar los datos fr\u00edos al almacenamiento m\u00e1s denso<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading has-secondary-color has-text-color has-link-color wp-elements-e5930a6544a26717cec1e94519415c75\" id=\"h-distributed-training-acceleration\"><strong>Aceleraci\u00f3n de la formaci\u00f3n distribuida<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">La plataforma Pure Storage proporciona:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Baja latencia de lectura: <\/strong>En cl\u00fasteres de GPU geodistribuidos<\/li>\n\n\n\n<li><strong>Cero tiempos de inactividad de la reconstrucci\u00f3n: <\/strong>Durante la ampliaci\u00f3n de la capacidad<\/li>\n\n\n\n<li><strong>Alta tasa de aciertos de cach\u00e9: <\/strong>Para conjuntos de datos multimodales<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading has-secondary-color has-text-color has-link-color wp-elements-8e49fe50a629a22e2a9af718f0f2fcb5\" id=\"h-the-pure-storage-competitive-differentiation\"><strong>La diferenciaci\u00f3n competitiva de Pure Storage <\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pila de kernel Linux optimizada para Flash:<\/strong> Menor uso de la CPU<\/li>\n\n\n\n<li><strong>Geometr\u00eda RAID din\u00e1mica:<\/strong> Mantener un tiempo de actividad alto durante los picos de ingesta<\/li>\n\n\n\n<li><strong>API de orquestaci\u00f3n de cargas de trabajo de IA: <\/strong>Automatizaci\u00f3n de la colocaci\u00f3n de los datos basada en la topolog\u00eda de cl\u00faster de GPU <\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Al tratar el almacenamiento como un coprocesador de GPU, Pure Storage permite que las empresas maximicen el potencial de su infraestructura de IA.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-implementation-guidelines\"><strong>Directrices de implementaci\u00f3n<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Para alinear el rendimiento de la GPU y el almacenamiento, tenga en cuenta el siguiente ejemplo de Python:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-multi-agent-rag-frameworks\"><strong>Marcos RAG multiagente<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">La llegada de los LLM ha impulsado el desarrollo de paradigmas avanzados, como los agentes de IA y los sistemas RAG con m\u00faltiples agentes. A diferencia de las canalizaciones RAG convencionales, que realizan una recuperaci\u00f3n de un solo paso desde una fuente de conocimiento externa solitaria, los marcos RAG de m\u00faltiples agentes orquestan la recuperaci\u00f3n en m\u00faltiples agentes especializados, cada uno accediendo a fuentes de datos distintas. Esta arquitectura aumenta significativamente la complejidad y las exigencias de I\/O de almacenamiento de la carga y el control de los datos para guardar y restaurar el estado actual del modelo durante el entrenamiento.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">El rendimiento de la carga de datos est\u00e1 influido por varios factores de bajo nivel:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Cargando la composici\u00f3n del pipeline:<\/strong> Implica la ejecuci\u00f3n secuencial o paralela de las operaciones de I\/O de almacenamiento y las fases de preprocesamiento\/transformaci\u00f3n de datos.<\/li>\n\n\n\n<li><strong>Patrones de acceso de I\/O: <\/strong>Determinado por la estructura del conjunto de datos, la estrategia de muestreo y los requisitos de entrada espec\u00edficos del modelo (por ejemplo, acceso secuencial frente a acceso aleatorio).<\/li>\n\n\n\n<li><strong>Caracter\u00edsticas del subsistema de almacenamiento:<\/strong> Debe admitir lecturas de alto rendimiento y baja latencia para minimizar el tiempo de inactividad de la GPU debido a los cuellos de botella I\/O.<br\/><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">El rendimiento de los controles de seguridad se ve influido por los siguientes factores:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Gesti\u00f3n eficiente de los datos: <\/strong>Los checkpointing en el entrenamiento de modelos a gran escala exigen un gran ancho de banda de lectura y escritura para minimizar las interrupciones de entrenamiento durante las operaciones de almacenamiento y restauraci\u00f3n.<\/li>\n\n\n\n<li><strong>Archivos de control: <\/strong>Los checkpoints suelen estar compuestos por uno o m\u00e1s archivos, cada uno de los cuales est\u00e1 escrito por un proceso o subproceso dedicado, y se adhieren a un modelo de un solo creador para garantizar la coherencia.<\/li>\n\n\n\n<li><strong>Alta sobrecarga del almacenamiento: <\/strong>Para los modelos grandes y los trabajos de entrenamiento prolongados, los requisitos de almacenamiento agregado para los puntos de control peri\u00f3dicos pueden ser sustanciales, lo que requiere soluciones de almacenamiento optimizadas y programaci\u00f3n de I\/O para gestionar eficazmente la amplificaci\u00f3n de escritura y el uso del almacenamiento flash.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Los par\u00e1metros clave que afectan a la eficiencia I\/O de almacenamiento incluyen los tama\u00f1os de las muestras y los lotes, la concurrencia (n\u00famero de subprocesos de lector y escritor), el protocolo de I\/O y la estrategia de paralelismo, las operaciones de lectura as\u00edncrona y la efectividad de las capas de cach\u00e9. La optimizaci\u00f3n de estos componentes es fundamental para mantener el uso de la GPU y garantizar un rendimiento de entrenamiento escalable en los sistemas RAG con m\u00faltiples agentes.<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><em>Para obtener m\u00e1s informaci\u00f3n sobre la optimizaci\u00f3n de los pipelines de IA con Pure Storage, <a href=\"https:\/\/www.purestorage.com\/solutions\/ai.html\" target=\"_blank\" rel=\"noreferrer noopener\">visite nuestra p\u00e1gina de soluciones de IA<\/a>.<\/em><\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><em>Obtenga m\u00e1s informaci\u00f3n sobre <a href=\"https:\/\/www.purestorage.com\/partners\/technology-alliance-partners\/nvidia.html\" target=\"_blank\" rel=\"noreferrer noopener\">nuestra colaboraci\u00f3n con NVIDIA<\/a>.<\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:30%\">\n<div class=\"wp-block-group sticky-content has-mint-green-500-background-color has-background is-layout-flow wp-block-group-is-layout-flow\" style=\"border-radius:20px;padding-top:20px;padding-right:20px;padding-bottom:20px;padding-left:20px\">\n<h2 class=\"wp-block-heading has-text-align-center has-ash-gray-500-color has-text-color has-link-color wp-elements-e03b5ed8732ebcc2a11722d5fcc4b3e7\" id=\"h-title\" style=\"font-size:px\">Garantice el \u00e9xito de la IA <\/h2>\n\n\n\n<p class=\"has-text-align-center has-ash-gray-500-color has-text-color has-link-color wp-elements-9223d3748670e48fb7ad2356cd860fe8 wp-block-paragraph\" style=\"font-size:clamp(0.875rem, 0.875rem + ((1vw - 0.2rem) * 0.208), 1rem);\">Obtenga m\u00e1s informaci\u00f3n sobre la plataforma de almacenamiento de datos m\u00e1s potente del mundo para la IA.\u00a0<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-f8bdad00 wp-block-buttons-is-layout-flex\" style=\"margin-top:1em;margin-bottom:1em\">\n<div class=\"wp-block-button is-style-outline button-sticky is-style-outline--2\"><a class=\"wp-block-button__link has-cloud-white-500-color has-basil-green-500-background-color has-text-color has-background has-link-color has-inter-font-family has-custom-font-size wp-element-button\" href=\"https:\/\/www.purestorage.com\/solutions\/ai.html\" style=\"border-style:none;border-width:0px;border-radius:4px;padding-top:14px;padding-right:16px;padding-bottom:14px;padding-left:16px;font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.208), 16px);\" target=\"_blank\" rel=\"noreferrer noopener\">Explore the AI Solutions Page<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Cuando se trata de GPU, \u00bfc\u00f3mo traduce la telemetr\u00eda de la infraestructura (umbrales de latencia, ratios de vataje, tasas de uso) en propuestas de valor preparadas para la sala de juntas?<\/p>\n","protected":false},"author":456,"featured_media":165803,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[14633],"tags":[14588,14849],"content-position":[],"ppma_author":[14189],"class_list":["post-166101","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-purely-technical","tag-ai-and-machine-learning-es","tag-flashblade-s-es"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Consiga un rendimiento de GPU completo en los pipelines de IA con FlashBlade\/\/S | Everpure Blog<\/title>\n<meta name=\"description\" content=\"Cuando se trata de una GPU, \u00bfc\u00f3mo traduce los umbrales de latencia, las ratios de vataje y el rendimiento en propuestas de valor preparadas para la sala de juntas?\" 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