Computer Architecture · All levels

Performance Counters and Telemetry — Worked Example

Worked Example for Performance Counters and Telemetry (Performance Analysis).

Scenario

A product workload exposes a Performance Counters and Telemetry issue late in architecture review.

Timeline

Metric fails at review meeting
Engineer captures metric report, trace snippet, and workload phase
Root cause traced to incorrect assumption from prior stage
Minimal architecture change or policy experiment applied and documented
Workload regression matrix re-run on the tagged model

Outcome

Architecture decision accepted with documented metric movement, risk, and validation proof.

Senior debrief

After solving the example, write the debrief a lead would expect: what changed, why it worked, what could regress, and what permanent methodology update prevents recurrence.

diagram

STAFF ARCHITECTURE REVIEW MEMO — Performance Analysis / Performance Counters and Telemetry

1. Current state
   - Failing / watched metric: Counter integrity and stall attribution dashboard
   - Workload / benchmark / trace: <fill before review>
   - Model tag, RTL tag, simulator version, PMU setup: <fill before review>
   - Scope: core, cache level, NoC path, coherency domain, accelerator, or SoC budget

2. Root-cause hypothesis
   - Most likely mechanism: <name pipeline/cache/NoC/coherency/perf mechanism>
   - Competing hypothesis: <name the second plausible cause>
   - Evidence still missing: <counter, trace, waveform, model sweep, or workload slice>

3. Proposed action
   - Minimal reversible change: <microarchitecture, policy, sizing, traffic, or software contract change>
   - Expected improvement: <metric delta>
   - Regression risk: Bad counter trust can send teams toward expensive but irrelevant optimizations.

4. Regression and signoff
   - Re-run: Counter integrity and stall attribution dashboard
   - Must not regress: RTL changes, compiler strategy, firmware scheduler tuning, and KPI commitments.
   - Decision owner: architecture owner

Before / after metric graph

diagram

METRIC TREND GRAPH — Performance Counters and Telemetry

IPC / throughput
  ^
  |                  target
  |                 ─ ─ ─ ─ ─ ─ ─
  |            ● after bounded fix
  |         /
  |    ● baseline
  |  /
  |● failing run
  +---------------------------------> experiment index
    bad tag       hypothesis        accepted fix

Readout rule:
  - one dot is not a conclusion
  - compare against same workload, seed, model tag, and counter setup
  - explain why the fix moved the metric, not just that it moved

Architecture deep dive

PMU evidence beats intuition for architecture decisions.

Concept diagram

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

diagram

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.

Practice this lesson