Computer Architecture · All levels

ACE and CHI Protocol Introduction — Reports & Metrics

Reports & Metrics for ACE and CHI Protocol Introduction (Coherency and Memory Ordering).

On-call / interview prompt

Which report lines prove ACE and CHI Protocol Introduction is healthy vs failing?

diagram

ARCHITECTURE ANALYSIS CHAIN

1. METRIC     — IPC, CPI, MPKI, bandwidth, latency, queue depth, stall cycles
2. HYPOTHESIS — microarch or system cause ordered by likelihood
3. EXPERIMENT — trace, PMU counter, simulation, or RTL probe
4. CHANGE      — pipeline, cache, NoC, or memory hierarchy adjustment
5. VALIDATION  — workload replay, regression suite, PPA impact

Reports to inspect

Protocol attribute mapping validation report
Channel utilization and credit pressure summary
Barrier and atomic completion correctness report

diagram

ACE-CHI MIGRATION HEALTH
  attribute_mapping_coverage: 100%
  unmapped_ordering_cases: 0
  credit_starvation_events: 2
  barrier_completion_mismatches: 1
  root_cause: completion_ack_reorder_under_backpressure
  fix_status: patch_validated_in_stress_regression

Smoke check (5 minutes)

Can you name the single worst line in the report?
Can you tie that line to a workload phase, structure, master, or data movement pattern?

How to read this like an architecture lead

The report is not a pass/fail artifact; it is a prioritization tool. Read ACE/CHI interoperability readiness report by severity, locality, trend, and fix cost before touching the design.

Report triage order

Confirm workload, model tag, seed, counter definitions, and warmup window.
Separate product blockers from exploratory tuning opportunities.
Cluster failures by workload phase, master, cache level, NoC path, coherency state, or accelerator kernel.
Compare against previous tag to identify new regressions, not just absolute failures.
Translate the worst line into an owner, experiment, and rollback plan.

diagram

SENIOR REPORT READOUT
  worst_line: <copy exact report line>
  cluster: <workload phase / master / cache level / NoC path / coherency state>
  delta_from_previous: <new/worse/better/same>
  first_experiment: <cheap evidence-gathering action>
  decision: <change design / assign owner / keep risk with approval / stop release>

Metric graph to sketch in review

diagram

REPORT GRAPH — ACE/CHI interoperability readiness report

stall contribution (% cycles)

frontend      ████████████  24
backend       ██████████████████  36
memory        ████████████████████████  48
fabric/qos    ████████  16
coherency     ██████████  20

How to read:
  1. Identify the dominant bar, not the noisiest anecdote.
  2. Cross-check with at least one independent artifact: trace, PMU, sim log, or waveform.
  3. If the dominant bar does not match the proposed fix, stop and reform the hypothesis.

Trend graph

diagram

METRIC TREND GRAPH — ACE and CHI Protocol Introduction

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

Coherency protocols trade traffic, latency, and verification complexity.

Concept diagram

diagram

MESI STATE SKETCH

        read miss          write
 Invalid ─────────► Shared ───────► Modified
    ▲                 │  ▲             │
    │ invalidate      │  │ downgrade   │ writeback
    └─────────────────┘  └─────────────┘

The interview bar is not naming states; it is explaining traffic and ordering.

Metric graph

diagram

COHERENCY TRAFFIC STACK

read shared      █████████████  42%
read exclusive   ███████        21%
invalidates      ██████████     31%
writebacks       █████          14%
snoop retries    ███            8%

False sharing often appears as invalidation spikes.

Metrics and artifacts

coherency transaction rate
snoop/filter efficiency
ordering violation tests
false sharing counters

Mini case study

Performance regression traced to false sharing on a counter array — coherency traffic exploded. Architecture fix: per-core counters + periodic merge, not faster NoC alone.

Debug branches

If rare SW bug, run litmus and ordering tests before microarch changes.
If traffic high, profile sharing patterns at cache-line granularity.

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.

Read the numbers

Practice this lesson