{"id":166105,"date":"2025-06-23T12:38:58","date_gmt":"2025-06-23T19:38:58","guid":{"rendered":"https:\/\/blog.everpuredata.com\/?p=166105"},"modified":"2025-11-10T12:54:56","modified_gmt":"2025-11-10T20:54:56","slug":"pure-storage-optimizing-gpu-performance-utilization-for-ai-workloads","status":"publish","type":"post","link":"https:\/\/blog.everpuredata.com\/it\/purely-technical\/pure-storage-optimizing-gpu-performance-utilization-for-ai-workloads\/","title":{"rendered":"In che modo Pure Storage elimina i colli di bottiglia computazionali, ottimizzando l&#8217;utilizzo della GPU per i workload di AI"},"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\">Sintesi<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">La piattaforma Pure Storage affronta le sfide tecniche dei moderni workload di AI, consentendo alle organizzazioni di massimizzare il potenziale della propria infrastruttura AI.<\/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\/it\/wp-json\/wp\/v2\/posts\/166105?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=\"Visualizza PDF\" \/><\/a><a href=\"https:\/\/blog.everpuredata.com\/it\/wp-json\/wp\/v2\/posts\/166105?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=\"Stampa contenuto\" \/><\/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\">Immagina che un&#8217;azienda abbia appena effettuato un investimento di 100.000 dollari, o addirittura 1 milione di dollari, in un cluster GPU per l&#8217;AI, ma solo il 62% di queste GPU viene utilizzato in modo coerente alla capacit\u00e0. Ci\u00f2 potrebbe comportare notevoli sprechi finanziari e perdita di ROI.\u00a0<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Tuttavia, i proprietari dell&#8217;infrastruttura possono prendere una decisione cruciale per evitare queste perdite, non solo finanziarie, ma anche di performance, efficienza e opportunit\u00e0. Inizia osservando un&#8217;infrastruttura di data storage con performance inferiori, che pu\u00f2 influire notevolmente sulle performance della GPU e sprecare i cicli della GPU.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Negli ambienti di AI, massimizzare l&#8217;utilizzo della GPU \u00e8 fondamentale per operazioni efficienti. Pure Storage risolve queste sfide fornendo architetture di storage progettate per ottimizzare l&#8217;utilizzo della GPU. Vediamo come.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-technical-constraints-and-solutions\"><strong>Vincoli tecnici e soluzioni<\/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\">piattaforma Pure Storage <\/a> affronta tre vincoli tecnici chiave:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Latenza di acquisizione dati:<\/strong> Riduzione dei tempi di attesa degli I\/O per garantire un flusso di dati continuo<\/li>\n\n\n\n<li><strong>Limiti di simultaneit\u00e0:<\/strong> Miglioramento delle funzionalit\u00e0 di formazione multi-GPU <\/li>\n\n\n\n<li><strong>Variabilit\u00e0 della velocit\u00e0 di trasmissione: <\/strong>Gestione dei burst di inferenza per performance costanti<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-gpu-storage-interdependence-in-ai-pipelines\"><strong>Interdipendenza dello storage GPU nelle pipeline di AI <\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">I workload AI moderni richiedono una data delivery parallelizzata che corrisponda alla larghezza di banda della memoria della GPU. Ad esempio, le GPU NVIDIA Blackwell richiedono una larghezza di banda di memoria aggregata elevata. Pure Storage<sup>\u00ae<\/sup> <a href=\"https:\/\/www.purestorage.com\/products\/unstructured-data-storage\/flashblade-s.html\" target=\"_blank\" rel=\"noreferrer noopener\">FlashBlade\/\/S<\/a>\u2122 offre performance elevate tramite:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Ottimizzazione del protocollo NVMe-oF:<\/strong> Miglioramento dell&#8217;efficienza di trasferimento dei dati<\/li>\n\n\n\n<li>Moduli<strong> DirectFlash\u00ae basati su ARM<\/strong>:<strong><sup><\/sup><\/strong><strong> <\/strong> Riduzione dei costi generali di gestione dello stack software<\/li>\n\n\n\n<li><strong>Ottimizzazione dinamica della parit\u00e0:<\/strong> Ottimizzazione dei workload misti di lettura\/scrittura<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Questa architettura riduce notevolmente i cicli di stallo dei dati, mantenendo saturi i core dei GPU tensori GPU.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-technical-benchmark-storage-impact-on-training-efficiency\"><strong>Benchmark tecnico: Impatto dello storage sull&#8217;efficienza della formazione<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Metrica<\/strong><\/td><td><strong>Storage HDD tradizionale<\/strong><\/td><td><strong>Soluzioni all-flash Pure Storage <\/strong><\/td><td><strong>Impatto sulla formazione<\/strong><\/td><\/tr><tr><td>Periodo di epoca<\/td><td>3-5 volte pi\u00f9 lungo<\/td><td>Basale (1x)<\/td><td>Lo storage flash pu\u00f2 ridurre i tempi di addestramento del 50-70% rispetto alle unit\u00e0 HDD<\/td><\/tr><tr><td>Utilizzo della GPU <\/td><td>30-60%<\/td><td>85-98%<\/td><td>Un utilizzo pi\u00f9 elevato significa che le GPU impiegano meno tempo ad aspettare i dati<\/td><\/tr><tr><td>Efficienza energetica (FLOPS\/watt)<\/td><td>TCO<\/td><td>2-3 volte superiore<\/td><td>Le soluzioni all-flash consentono una maggiore capacit\u00e0 di calcolo per watt di potenza<\/td><\/tr><tr><td>Latenza di lettura<\/td><td>5-10 ms<\/td><td>0,2-1 ms<\/td><td>La latenza inferiore garantisce l&#8217;alimentazione tempestiva dei dati da parte delle GPU<\/td><\/tr><tr><td>Velocit\u00e0 di trasmissione<\/td><td>100-200 MB\/s per unit\u00e0<\/td><td>5-20 GB\/s<\/td><td>Una velocit\u00e0 di trasmissione pi\u00f9 elevata impedisce la carenza di dati<\/td><\/tr><tr><td>IOPS<\/td><td>100-200 per unit\u00e0<\/td><td>Oltre 100.000<\/td><td>Crucial per modelli di accesso casuale in set di dati di grandi dimensioni<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-solving-next-gen-ai-workload-challenges\"><strong>Risolvere le sfide dei workload di AI di nuova generazione<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In termini di utilizzo della GPU, la piattaforma Pure Storage offre:<\/p>\n\n\n\n<h4 class=\"wp-block-heading has-secondary-color has-text-color has-link-color wp-elements-68826e1fbba95ae3688a6734f9e94b3b\" id=\"h-retrieval-augmented-generation-rag-optimization\"><strong>Ottimizzazione RAG (Retrieval-Augmented Generation)<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Una soluzione RAG congiunta di Pure Storage e NVIDIA include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Direct storage GPU: <\/strong>Superare i colli di bottiglia della CPU<\/li>\n\n\n\n<li><strong>pipeline indicizzate ai Metadata: <\/strong>Riduzione della latenza dei prompt LLM<\/li>\n\n\n\n<li><strong>Velocit\u00e0 di trasmissione controllata da QoS: <\/strong>Garantire performance costanti<\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><a href=\"https:\/\/blog.everpuredata.com\/perspectives\/optimize-genai-apps-with-rag-from-pure-storage-and-nvidia\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Scopri di pi\u00f9 sulla soluzione 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-8536d04d58f99682fed5503c944305c9\" id=\"h-energy-efficient-scaling\"><strong>Scalabilit\u00e0 a risparmio energetico<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Compressione accelerata dall&#8217;hardware: <\/strong>Riduzione dell&#8217;ingombro dei dati<\/li>\n\n\n\n<li><strong>Tiering predittivo: <\/strong>Spostamento dei dati freddi in uno storage pi\u00f9 denso<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading has-secondary-color has-text-color has-link-color wp-elements-e7f5608519fb91ff2a9d5ed5145cbe30\" id=\"h-distributed-training-acceleration\"><strong>Accelerazione della formazione distribuita<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">La piattaforma Pure Storage offre:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bassa latenza di lettura: <\/strong>Nei cluster GPU geo-distribuiti<\/li>\n\n\n\n<li><strong>Zero downtime di ricostruzione: <\/strong>Durante l&#8217;espansione della capacit\u00e0<\/li>\n\n\n\n<li><strong>Tasso di hit della cache elevato: <\/strong>Per dataset multimodali<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading has-secondary-color has-text-color has-link-color wp-elements-8325d7e6291f7fa1d51539f1797601c7\" id=\"h-the-pure-storage-competitive-differentiation\"><strong>La differenziazione competitiva di Pure Storage <\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>kernel stack Linux ottimizzato per Flash:<\/strong> Minore utilizzo della CPU<\/li>\n\n\n\n<li><strong>Geometria RAID dinamica:<\/strong> Mantenimento di un tempo di attivit\u00e0 elevato durante i picchi di ingest<\/li>\n\n\n\n<li><strong>API di orchestrazione dei workload AI: <\/strong>Automazione del posizionamento dei dati in base alla topologia dei cluster GPU <\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Trattando lo storage come un coprocessore GPU, Pure Storage consente alle aziende di massimizzare il potenziale della propria infrastruttura AI.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-implementation-guidelines\"><strong>Linee guida per l&#8217;implementazione<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Per allineare le performance di GPU e storage, considera il seguente esempio 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>Framework RAG multi-agente<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">L&#8217;avvento degli LLM ha guidato lo sviluppo di paradigmi avanzati come gli agenti AI e i sistemi RAG multi-agente. A differenza delle pipeline RAG tradizionali, che eseguono il recupero a passaggio singolo da una sola fonte di conoscenza esterna, i framework RAG multi-agente orchestrano il recupero tra pi\u00f9 agenti specializzati, ciascuno dei quali accede a origini dati distinte. Questa architettura aumenta notevolmente la complessit\u00e0 e le esigenze di I\/O di storage per il caricamento e il checkpoint dei dati, al fine di salvare e ripristinare lo stato attuale del modello durante l&#8217;addestramento.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Le performance di caricamento dei dati sono influenzate da diversi fattori di basso livello:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Caricamento della composizione della pipeline:<\/strong> Prevede l&#8217;esecuzione sequenziale o parallela delle operazioni di I\/O di storage e delle fasi di pre-elaborazione\/trasformazione dei dati<\/li>\n\n\n\n<li><strong>Modelli di accesso I\/O: <\/strong>Determinato in base alla struttura del set di dati, alla strategia di campionamento e ai requisiti di input specifici del modello (ad es. accesso sequenziale o casuale)<\/li>\n\n\n\n<li><strong>Caratteristiche dei sottosistemi di storage:<\/strong> Deve supportare letture a bassa latenza e velocit\u00e0 di trasmissione elevata per ridurre al minimo il tempo di inattivit\u00e0 della GPU a causa dei colli di bottiglia degli I\/O <br\/><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Le performance di controllo sono influenzate dai seguenti fattori:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Gestione efficiente dei dati: <\/strong>Il checkpoint nell&#8217;addestramento dei modelli su larga scala richiede un&#8217;elevata larghezza di banda in lettura e scrittura per ridurre al minimo le interruzioni dell&#8217;addestramento durante le operazioni di salvataggio e restore.<\/li>\n\n\n\n<li><strong>File di check-point: <\/strong>I punti di controllo sono in genere costituiti da uno o pi\u00f9 file, ciascuno dei quali \u00e8 scritto da un processo o thread dedicato, che aderisce a un modello a scrittura singola per garantire la coerenza.<\/li>\n\n\n\n<li><strong>Costi generali di storage elevati: <\/strong>Per i modelli di grandi dimensioni e i lavori di addestramento prolungati, i requisiti di storage aggregato per i checkpoint periodici possono essere sostanziali, richiedendo soluzioni di storage ottimizzate e pianificazione degli I\/O per gestire efficacemente l&#8217;amplificazione in scrittura e l&#8217;utilizzo dello storage flash.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">I parametri chiave che influiscono sull&#8217;efficienza degli I\/O di storage includono le dimensioni di campioni e batch, la simultaneit\u00e0 (numero di thread di lettori e scrittori), il protocollo I\/O e la strategia di parallelismo, le operazioni di lettura asincrona e l&#8217;efficacia dei livelli di cache. L&#8217;ottimizzazione di questi componenti \u00e8 fondamentale per sostenere l&#8217;utilizzo delle GPU e garantire performance di addestramento scalabili nei sistemi RAG multi-agente.<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><em>Per ulteriori informazioni sull&#8217;ottimizzazione delle pipeline di AI con Pure Storage, <a href=\"https:\/\/www.purestorage.com\/solutions\/ai.html\" target=\"_blank\" rel=\"noreferrer noopener\">visita la nostra pagina dedicata alle soluzioni di AI<\/a>.<\/em><\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><em>Scopri di pi\u00f9 sulla <a href=\"https:\/\/www.purestorage.com\/partners\/technology-alliance-partners\/nvidia.html\" target=\"_blank\" rel=\"noreferrer noopener\">nostra partnership 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-c52ce96d7a06ba1237ac97e8eff8f4e9\" id=\"h-title\" style=\"font-size:px\">Garantisci il successo dell&#8217;AI <\/h2>\n\n\n\n<p class=\"has-text-align-center has-ash-gray-500-color has-text-color has-link-color wp-elements-f79abaa5ec76df2d4a63100ce3c33cfe wp-block-paragraph\" style=\"font-size:clamp(0.875rem, 0.875rem + ((1vw - 0.2rem) * 0.208), 1rem);\">Scopri di pi\u00f9 sulla piattaforma di data storage pi\u00f9 potente al mondo per l&#8217;AI.\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>Quando si tratta di GPU, come si traduce la telemetria dell&#8217;infrastruttura (soglie di latenza, rapporti di wattaggio, tassi di utilizzo) in proposte di valore predisposte per il consiglio di amministrazione?<\/p>\n","protected":false},"author":456,"featured_media":165799,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[13130],"tags":[13936,13939],"content-position":[],"ppma_author":[14189],"class_list":["post-166105","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-purely-technical","tag-ai-and-machine-learning-it","tag-flashblade-s-it"],"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>Sfrutta le performance complete della GPU nelle pipeline AI con FlashBlade\/\/S | Pure Storage Blog<\/title>\n<meta name=\"description\" content=\"Quando si tratta di una GPU, come si traducono le soglie di latenza, i rapporti di wattaggio e le performance in proposte di valore predisposte per il consiglio di amministrazione?\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/blog.everpuredata.com\/it\/purely-technical\/pure-storage-optimizing-gpu-performance-utilization-for-ai-workloads\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"In che modo Pure Storage elimina i colli di bottiglia computazionali, ottimizzando l&#039;utilizzo della GPU per i workload di AI\" \/>\n<meta property=\"og:description\" content=\"Quando si tratta di una GPU, come si traducono le soglie di latenza, i rapporti di wattaggio e le performance in proposte di valore predisposte per il consiglio di amministrazione?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/blog.everpuredata.com\/it\/purely-technical\/pure-storage-optimizing-gpu-performance-utilization-for-ai-workloads\/\" \/>\n<meta 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