Bottleneck Analysis Framework — Extended Case Study

Extended case study

A review is called because a workload regresses after a Bottleneck Analysis Framework change.

Background

A stable baseline existed until a Performance Analysis change improved one benchmark and regressed a product workload on architecture KPI dashboard.

Symptoms observed

• Regression in architecture KPI dashboard

• Sim vs silicon disagreement

• Pressure to revert or ship risk

Investigation timeline

1. Freeze tags

2. Reproduce

3. Cluster

4. Experiment

5. Validate

6. Memo

Root cause

Queue scheduling and burst shaping restore overlap, improving end-to-end throughput without frequency escalation.

Fix and validation

• Bound change

• Replay workloads

• Check downstream impact

Lessons learned

• Workload coverage beats clever microarchitecture

• Every change needs rollback triggers

CASE STUDY — Bottleneck Analysis Framework
baseline/regressed/fixed metrics

Architecture deep dive

PMU evidence beats intuition for architecture decisions.

Concept diagram

TOP-DOWN PERFORMANCE METHOD

Total cycles
 ├─ Retiring useful work
 ├─ Frontend bound
 ├─ Bad speculation
 ├─ Backend core bound
 └─ Backend memory bound

Only after classification should you propose cache, branch, pipeline, or NoC changes.

Metric graph

ROOFLINE SKETCH

Performance
  ^
  |                     compute roof
  |-------------------------------
  |                   /
  |                 /
  |               /   ● workload A (compute-bound)
  |             /
  |   ● workload B (memory-bound)
  +---------------------------------> arithmetic intensity
        memory bandwidth slope

Metrics and artifacts

• PMU event sets

• roofline chart

• top-down stall breakdown

• workload sensitivity matrix

Mini case study

Team proposed wider SIMD but roofline showed memory-bound kernel — bandwidth upgrade and locality fix delivered 2× speedup at lower area cost.

Debug branches

• If counters disagree with sim, align workload and warmup.

• If bottleneck unclear, use top-down method before microarch tweaks.

Senior review question

Ask: what single metric would prove this concept is working or failing on your workload?

Key takeaways

• Connect every architecture claim to a workload and measurable metric.

• State verification and PPA impact before proposing design changes.

Common pitfalls

• Feature-driven design without MPKI/IPC/bandwidth evidence.

• Ignoring coherency and NoC traffic in cache and accelerator sizing.

Study notes

Re-read this topic with one concrete workload.