GPU Driver & Software Stack Release Validation
Validating driver, CUDA or ROCm, firmware and framework combinations as a test matrix: pruning it, tiering tests on real GPU pools, catching performance regressions through noise, bisecting builds and calling go or no-go.
Overview
Why this topic
A GPU driver release is not one artifact. It ships into a world of GPU models, firmware versions, operating systems and kernels, CUDA or ROCm toolkits, math and communication libraries, and frameworks like PyTorch, and customers run every combination. A change that is correct on one cell of that matrix can hang on another, or run 8 percent slower only on one GPU with one NCCL version.
The release validation engineer owns the answer to one question before every release: is this combination safe to ship? That means choosing which cells to test, running them on a limited pool of real GPUs, telling a real performance regression from noise, finding the build that caused a regression, and writing down the go or no-go call with evidence.
Interview questions
- You have 600 possible configurations and GPU capacity for about 60 per night. How do you choose which 60?
- How do you detect a 3 percent performance regression when run-to-run noise is 2 percent?
- A nightly build regressed throughput on one GPU model. The night contained 140 driver changes. How do you find the culprit?
- How do you organize a limited pool of real GPUs between per-commit, nightly and release work?
- What are the go or no-go criteria for a driver release, and how do you run that decision?
- Customers report that after upgrading the driver, some training jobs hang on multi-GPU nodes. Validation passed. What went wrong, and what do you change?
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