{"id":51157,"date":"2026-05-22T08:00:00","date_gmt":"2026-05-22T15:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/en-us\/blog\/?p=51157"},"modified":"2026-05-21T14:39:01","modified_gmt":"2026-05-21T21:39:01","slug":"azure-netapp-files-for-eda-workloads-from-revolution-to-breakthrough-at-scale","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/azure-netapp-files-for-eda-workloads-from-revolution-to-breakthrough-at-scale\/","title":{"rendered":"Azure NetApp Files for EDA workloads: From revolution to breakthrough at scale"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\" id=\"last-year-we-outlined-how-azure-netapp-files-was-helping-reshape-silicon-design-by-delivering-the-low-latency-high-throughput-storage-required-for-eda-workloads-at-cloud-scale-since-then-we-have-continued-to-extend-performance-and-scalability-today-we-are-advancing-that-progress-with-another-significant-step-forward\">Last year, we outlined how <a href=\"https:\/\/azure.microsoft.com\/blog\/azure-netapp-files-revolutionizing-silicon-design-for-high-performance-computing\/\">Azure NetApp Files helped reshape silicon design<\/a> by delivering the low-latency, high-throughput storage required for Electronic Design Automation (EDA) workloads at cloud scale. Since then, we have continued to extend performance and scalability. Today, we are advancing that progress with another significant step forward.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Modern semiconductor design is defined by scale. Thousands of concurrent EDA jobs spanning simulation, synthesis, and verification run continuously against shared datasets, where even small variations in storage latency can ripple across entire design cycles. For many teams, this has historically limited how far EDA workflows could scale in the cloud. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That constraint is now changing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" id=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" target=\"_blank\" rel=\"noreferrer noopener\">Azure NetApp Files (ANF)<\/a> is redefining what is possible for EDA in the cloud by delivering predictable, high-performance shared storage at massive concurrency. With new independent benchmark results and growing adoption by leading semiconductor companies, Azure is establishing itself as a viable\u2014and in many cases superior\u2014platform for modern EDA environments. <\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-a89b3969 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/azure-netapp-files\/solutions-benefits-azure-netapp-files-electronic-design-automation\" target=\"_blank\" rel=\"noreferrer noopener\">See how Azure NetApp Files delivers scalable, high-performance storage for EDA<\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"why-eda-storage-has-been-difficult-to-scale-in-the-cloud\">Why EDA storage has been difficult to scale in the cloud<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">EDA workloads combine three characteristics that have traditionally challenged cloud storage architectures:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"wp-block-list-item\">Extremely high concurrency, with thousands of jobs accessing shared file systems simultaneously.  <\/li>\n\n\n\n<li class=\"wp-block-list-item\">Strict latency sensitivity, where even minor delays reduce compute efficiency and extend runtimes.  <\/li>\n\n\n\n<li class=\"wp-block-list-item\">Intensive shared data access patterns, creating contention under load.  <\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">While cloud compute scales easily, shared storage has often introduced variability that limits overall system efficiency. As concurrency increases, storage becomes the bottleneck, impacting regression cycles, increasing tool license costs, and slowing time to tape-out.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For EDA teams evaluating cloud transformation, the central question has remained consistent: can storage scale with compute while maintaining predictable performance?<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"a-modern-approach-azure-netapp-files-for-eda-at-scale\">A modern approach: Azure NetApp Files for EDA at scale<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" id=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" target=\"_blank\" rel=\"noreferrer noopener\">Azure NetApp Files<\/a> is designed specifically to address this challenge. Its architecture aligns directly to the requirements of highly parallel, shared workloads like EDA. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">At its core, ANF enables independent scaling of compute and storage, so EDA clusters can grow without storage becoming the constraint, and additional compute nodes do not introduce hotspots or contention at the storage layer. It natively supports concurrent metadata operations at scale, handling the millions of small file interactions typical of EDA workflows without degradation. And its service-level performance model ensures that throughput and IOPS scale predictably with capacity, eliminating the need for complex tuning.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">More recently, innovations such as large volumes and large volumes breakthrough mode have expanded the concurrency envelope even further. These capabilities allow thousands of parallel jobs to share a single storage environment while maintaining consistent latency under sustained load.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This delivers what cloud-based EDA systems have long struggled to provide: consistent, repeatable performance, not only at low utilization, but also under full production load.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"independent-validation-specstorage-solution-2020-benchmark-results\">Independent validation: SPECstorage\u00ae Solution 2020 benchmark results<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">To validate these capabilities in a real-world context, <a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" id=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" target=\"_blank\" rel=\"noreferrer noopener\">Azure NetApp Files<\/a> was measured using the industry-standard <a href=\"https:\/\/www.spec.org\/storage2020\/results\/eda_blended\/\" id=\"https:\/\/www.spec.org\/storage2020\/results\/eda_blended\/\" target=\"_blank\" rel=\"noreferrer noopener\">SPECstorage\u00ae Solution 2020 EDA_BLENDED<\/a> benchmark. This benchmark simulates realistic EDA workflows by combining metadata-intensive frontend operations with throughput-heavy backend processing, all under strict latency requirements. <\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>The Azure NetApp Files large volume breakthrough mode scale configuration reached 17,280 SPECstorage\u00ae Solution 2020 EDA_BLENDED JOBS with an overall response time of 0.60 milliseconds (ms).<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">These results demonstrate several important characteristics:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"wp-block-list-item\">The ability to sustain very high levels of concurrent EDA workloads.  <\/li>\n\n\n\n<li class=\"wp-block-list-item\">Consistently low response times under load.  <\/li>\n\n\n\n<li class=\"wp-block-list-item\">Linear scaling behavior as concurrency increases.  <\/li>\n\n\n\n<li class=\"wp-block-list-item\">No requirement for overprovisioning.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">\n  Historically, top benchmark outcomes in this category have been associated with tightly integrated on-premises systems. This validation underscores a broader shift in the industry: when architected correctly, <strong>cloud-based EDA infrastructure can not only match on-premises approaches, but in some scenarios surpass them in both scale and operational efficiency.<\/strong>\n<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"proven-in-production-eda-workloads-already-running-on-anf\">Proven in production: EDA workloads already running on ANF<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This performance is not limited to benchmarks. Organizations such as AMD and ASML are already using Azure NetApp Files to run EDA and high-performance design workloads in production environments.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These companies operate at the leading edge of semiconductor innovation, where infrastructure must support both extreme scale and precise predictability. Their adoption of ANF reflects a broader industry trend: moving EDA workloads to the cloud is no longer experimental, it is becoming a strategic advantage.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These customers, along with others, consistently report the same operational benefits:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"wp-block-list-item\">The ability to increase regression concurrency without performance degradation.  <\/li>\n\n\n\n<li class=\"wp-block-list-item\">Improved utilization of compute resources and reduced EDA tool license fees.  <\/li>\n\n\n\n<li class=\"wp-block-list-item\">Greater predictability in design cycles, enabling more confident scheduling of key milestones.  <\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">In this context, storage is no longer the limiting factor\u2014it becomes an enabler of scale.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-azure-helps-eda-teams-scale-with-confidence\">How Azure helps EDA teams scale with confidence<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Organizations have flexibility in how they deploy EDA environments with Azure NetApp Files, depending on workload characteristics and operational priorities.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Some teams choose a centralized model built around a single large volume to maximize throughput and tightly control latency. Others adopt a multi-volume approach to distribute workloads and scale concurrency across different job types. Many enterprises extend existing on-premises environments into Azure, using cloud capacity to absorb peak demand without permanent infrastructure expansion.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Across all of these patterns, one principle remains consistent: storage performance must scale predictably alongside compute. Azure NetApp Files provides that foundation.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-large-font-size wp-block-paragraph\">Azure NetApp Files delivers the consistent, high\u2011throughput NFS performance that modern EDA workloads demand, shrinking runtimes, accelerating tape\u2011out schedules, and giving chip designers the confidence that storage will never be the bottleneck.<\/p>\n<cite>Srikanth Gubbala, Head of Global HPC Infrastructure, Applied Materials<\/cite><\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bringing-it-all-together\">Bringing it all together<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The evolution of cloud storage for EDA marks an important inflection point for the semiconductor industry. What was once considered a tradeoff\u2014scale versus predictability\u2014is no longer a constraint.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With Azure NetApp Files, organizations can confidently run highly concurrent EDA workloads in the cloud, supported by architecture designed for their specific demands and validated by independent benchmarking.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For teams exploring how to modernize their EDA infrastructure, the path forward is increasingly clear. Cloud-based storage can now meet the requirements of even the most demanding design environments, while offering the flexibility to scale as workloads continue to grow.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For a deeper technical exploration of the benchmark configuration and design considerations, see the companion Azure Tech Community technical blog:<a href=\"https:\/\/techcommunity.microsoft.com\/blog\/azurestorageblog\/from-scale-to-breakthrough-azure-netapp-files-sets-a-new-cloud-benchmark-for-eda\/4520890\" target=\"_blank\" rel=\"noreferrer noopener\"> <strong>\u201cFrom scale to breakthrough: Azure NetApp Files sets a new cloud benchmark for EDA.\u201d<\/strong><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For further information, explore the <a href=\"https:\/\/learn.microsoft.com\/azure\/azure-netapp-files\/\" target=\"_blank\" rel=\"noreferrer noopener\">Azure NetApp Files documentation<\/a> or email <a href=\"mailto:askanf@microsoft.com\">askanf@microsoft.com<\/a>.<\/p>\n\n\n\n<aside class=\"cta-block cta-block--align-left cta-block--has-image wp-block-msx-cta\" data-bi-an=\"CTA Block\">\n\t<div class=\"cta-block__content\">\n\t\t\t\t\t<div class=\"cta-block__image-container\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-1024x683.jpg\" class=\"cta-block__image\" alt=\"CLO20b_Jayesh_001\" srcset=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-1024x683.jpg 1024w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-300x200.jpg 300w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-768x512.jpg 768w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-1536x1024.jpg 1536w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-2048x1365.jpg 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/>\t\t\t<\/div>\n\t\t\n\t\t<div class=\"cta-block__body\">\n\t\t\t<h2 class=\"cta-block__headline\">Scale high-performance EDA workloads with Azure NetApp Files<\/h2>\n\t\t\t<p class=\"cta-block__text\">Discover how Azure NetApp Files delivers predictable, high-performance storage for EDA workloads, enabling massive concurrency, low latency, and consistent scaling in production.<\/p>\n\t\t\t\t\t\t\t<div class=\"cta-block__actions\">\n\t\t\t\t\t<a\n\t\t\t\t\t\thref=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\"\n\t\t\t\t\t\tclass=\"btn cta-block__link btn-link\"\n\t\t\t\t\t\ttarget=\"_blank\"\t\t\t\t\t>\n\t\t\t\t\t\tGet started with ANF\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t<\/div>\n<\/aside>\n","protected":false},"excerpt":{"rendered":"<p>Azure NetApp Files is redefining what\u2019s possible for EDA in the cloud\u2014delivering scalable, high-performance storage that supports massive concurrency, low latency, and consistent production performance. With independent benchmark validation and real-world adoption, organizations can now run EDA workloads at scale without traditional storage bottlenecks.<\/p>\n","protected":false},"author":113,"featured_media":51200,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ms_queue_id":[],"ep_exclude_from_search":false,"_classifai_error":"","_classifai_text_to_speech_error":"","_alt_title":"","footnotes":"","msx_community_cta_settings":[]},"categories":[1491],"tags":[],"audience":[3072,3053,3056],"content-type":[1481],"product":[1502],"tech-community":[2986],"topic":[],"coauthors":[80],"class_list":["post-51157","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-storage","audience-ai-professionals","audience-it-decision-makers","audience-it-implementors","content-type-thought-leadership","product-azure-netapp-files","review-flag-never-1680286580-606","review-flag-new-1680286579-546"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Azure NetApp 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