EDA in the Cloud: Hybrid Bursting, Storage & IP Security
Bursting chip design work to AWS, Azure or Google Cloud: which jobs move well, cloud file systems and caches (FSx for NetApp ONTAP, Azure NetApp Files, FlexCache), license reach, LSF and Slurm cloud connectors, ParallelCluster and CycleCloud, spot capacity, foundry and export-control constraints, and the cost model.
Overview
The job
Chip design demand is not flat. Regressions double before every milestone, library characterization arrives in waves with each PDK update, and the last weeks before tape-out need several times the signoff throughput of an ordinary month. CAD infrastructure and platform engineers at chip companies build the middle path between buying for the peak and slipping the date: a hybrid farm whose scheduler adds cloud hosts when the queue grows, whose cloud hosts see the same tools and design data as on-prem ones, and whose security satisfies foundries, IP vendors and export rules.
License servers, license-aware scheduling and the buy-versus-burst decision for the whole farm are in EDA Licenses & Compute Farms (eda-license-compute-mgmt); the CI tiers that create the regression load are in The Chip Design Flow as a CI Pipeline (chip-design-flow-ci).
Interview questions
- Design a hybrid farm that can burst 20,000 cores of regression and characterization to a public cloud within an hour of the queue growing.
- Leadership asks you to "move EDA to the cloud". How do you decide which job classes burst and which stay?
- Design the storage layer so cloud hosts see /tools, /proj and released IP exactly as on-prem hosts do.
- How do you protect foundry PDK data and design IP in a cloud environment, and how do foundry agreements and export controls shape the design?
- How do you use spot capacity for EDA without losing the schedule?
- Build the cost model for a 10-day burst and decide what goes to the cloud.
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