Why This Matters
AGI workloads run into performance limits that are often misunderstood. Roofline modeling gives a direct way to identify whether a workload is constrained by compute throughput or memory bandwidth, so optimization work can target the right bottleneck first.
What This Covers
This insight outlines a practical roofline workflow for AI kernels:
- Select target hardware and establish peak compute/bandwidth limits.
- Estimate arithmetic intensity for representative operations.
- Map kernels to the roofline and classify likely bottlenecks.
Build Next
- Add hardware presets for common accelerators.
- Add repeatable arithmetic-intensity templates for model components.
- Export chart data with assumptions attached for reproducibility.