Azure NetApp Recordsdata is redefining what’s attainable for EDA within the cloud—delivering scalable, high-performance storage that helps huge concurrency, low latency, and constant manufacturing efficiency. With impartial benchmark validation and real-world adoption, organizations can now run EDA workloads at scale with out conventional storage bottlenecks.
Final yr, we outlined how Azure NetApp Recordsdata helped reshape silicon design by delivering the low-latency, high-throughput storage required for Digital Design Automation (EDA) workloads at cloud scale. Since then, we’ve continued to increase efficiency and scalability. At present, we’re advancing that progress with one other important step ahead.
Trendy semiconductor design is outlined by scale. 1000’s of concurrent EDA jobs spanning simulation, synthesis, and verification run constantly in opposition to shared datasets, the place even small variations in storage latency can ripple throughout whole design cycles. For a lot of groups, this has traditionally restricted how far EDA workflows might scale within the cloud.
That constraint is now altering.
Azure NetApp Recordsdata (ANF) is redefining what is feasible for EDA within the cloud by delivering predictable, high-performance shared storage at huge concurrency. With new impartial benchmark outcomes and rising adoption by main semiconductor firms, Azure is establishing itself as a viable—and in lots of circumstances superior—platform for contemporary EDA environments.
Why EDA storage has been tough to scale within the cloud
EDA workloads mix three traits which have historically challenged cloud storage architectures:
- Extraordinarily excessive concurrency, with 1000’s of jobs accessing shared file techniques concurrently.
- Strict latency sensitivity, the place even minor delays cut back compute effectivity and lengthen runtimes.
- Intensive shared information entry patterns, creating competition beneath load.
Whereas cloud compute scales simply, shared storage has typically launched variability that limits general system effectivity. As concurrency will increase, storage turns into the bottleneck, impacting regression cycles, growing software license prices, and slowing time to tape-out.
For EDA groups evaluating cloud transformation, the central query has remained constant: can storage scale with compute whereas sustaining predictable efficiency?
A contemporary strategy: Azure NetApp Recordsdata for EDA at scale
Azure NetApp Recordsdata is designed particularly to handle this problem. Its structure aligns on to the necessities of extremely parallel, shared workloads like EDA.
At its core, ANF permits impartial scaling of compute and storage, so EDA clusters can develop with out storage changing into the constraint, and extra compute nodes don’t introduce hotspots or competition on the storage layer. It natively helps concurrent metadata operations at scale, dealing with the hundreds of thousands of small file interactions typical of EDA workflows with out degradation. And its service-level efficiency mannequin ensures that throughput and IOPS scale predictably with capability, eliminating the necessity for advanced tuning.
Extra lately, improvements similar to massive volumes and enormous volumes breakthrough mode have expanded the concurrency envelope even additional. These capabilities enable 1000’s of parallel jobs to share a single storage surroundings whereas sustaining constant latency beneath sustained load.
This delivers what cloud-based EDA techniques have lengthy struggled to supply: constant, repeatable efficiency, not solely at low utilization, but in addition beneath full manufacturing load.
Unbiased validation: SPECstorage® Answer 2020 benchmark outcomes
To validate these capabilities in a real-world context, Azure NetApp Recordsdata was measured utilizing the industry-standard SPECstorage® Answer 2020 EDA_BLENDED benchmark. This benchmark simulates sensible EDA workflows by combining metadata-intensive frontend operations with throughput-heavy backend processing, all beneath strict latency necessities.
| The Azure NetApp Recordsdata massive quantity breakthrough mode scale configuration reached 17,280 SPECstorage® Answer 2020 EDA_BLENDED JOBS with an general response time of 0.60 milliseconds (ms). |
These outcomes display a number of necessary traits:
- The power to maintain very excessive ranges of concurrent EDA workloads.
- Constantly low response occasions beneath load.
- Linear scaling conduct as concurrency will increase.
- No requirement for overprovisioning.
Traditionally, prime benchmark outcomes on this class have been related to tightly built-in on-premises techniques. This validation underscores a broader shift within the {industry}: when architected accurately, cloud-based EDA infrastructure can’t solely match on-premises approaches, however in some situations surpass them in each scale and operational effectivity.
Confirmed in manufacturing: EDA workloads already operating on ANF
This efficiency is just not restricted to benchmarks. Organizations similar to AMD and ASML are already utilizing Azure NetApp Recordsdata to run EDA and high-performance design workloads in manufacturing environments.
These firms function at the vanguard of semiconductor innovation, the place infrastructure should assist each excessive scale and exact predictability. Their adoption of ANF displays a broader {industry} pattern: shifting EDA workloads to the cloud is now not experimental, it’s changing into a strategic benefit.
These clients, together with others, constantly report the identical operational advantages:
- The power to extend regression concurrency with out efficiency degradation.
- Improved utilization of compute assets and lowered EDA software license charges.
- Higher predictability in design cycles, enabling extra assured scheduling of key milestones.
On this context, storage is now not the limiting issue—it turns into an enabler of scale.
How Azure helps EDA groups scale with confidence
Organizations have flexibility in how they deploy EDA environments with Azure NetApp Recordsdata, relying on workload traits and operational priorities.
Some groups select a centralized mannequin constructed round a single massive quantity to maximise throughput and tightly management latency. Others undertake a multi-volume strategy to distribute workloads and scale concurrency throughout totally different job sorts. Many enterprises lengthen present on-premises environments into Azure, utilizing cloud capability to soak up peak demand with out everlasting infrastructure enlargement.
Throughout all of those patterns, one precept stays constant: storage efficiency should scale predictably alongside compute. Azure NetApp Recordsdata supplies that basis.
Azure NetApp Recordsdata delivers the constant, excessive‑throughput NFS efficiency that fashionable EDA workloads demand, shrinking runtimes, accelerating tape‑out schedules, and giving chip designers the boldness that storage won’t ever be the bottleneck.
Srikanth Gubbala, Head of World HPC Infrastructure, Utilized Supplies
Bringing all of it collectively
The evolution of cloud storage for EDA marks an necessary inflection level for the semiconductor {industry}. What was as soon as thought-about a tradeoff—scale versus predictability—is now not a constraint.
With Azure NetApp Recordsdata, organizations can confidently run extremely concurrent EDA workloads within the cloud, supported by structure designed for his or her particular calls for and validated by impartial benchmarking.
For groups exploring find out how to modernize their EDA infrastructure, the trail ahead is more and more clear. Cloud-based storage can now meet the necessities of even probably the most demanding design environments, whereas providing the pliability to scale as workloads proceed to develop.
For a deeper technical exploration of the benchmark configuration and design issues, see the companion Azure Tech Group technical weblog: “From scale to breakthrough: Azure NetApp Recordsdata units a brand new cloud benchmark for EDA.”
For additional info, discover the Azure NetApp Recordsdata documentation or electronic mail askanf@microsoft.com.