Snoop & Cache Maintenance: Reports & Metrics

Reports and metrics

Reports & Metrics for Snoop & Cache Maintenance focuses on cache maintenance latency, invalidation count, stale data escapes. The goal is to connect the observable symptom to protocol mechanism, ownership, and regression risk.

The job of a report is to turn cache maintenance latency, invalidation count, stale data escapes into a decision. A single average number is almost never enough; you need the distribution, the traffic class breakdown, and a clear gap between legal maximum and product target.

Metric movement

METRIC GRAPH — cache maintenance latency, invalidation count, stale data escapes

throughput / success
  ^
  |                         target
  |                       - - - - - - -
  |                  o after bounded fix
  |              o
  |         o baseline
  |    o failing run
  +--------------------------------------> experiment
    config A     isolated root cause     accepted change

Readout:
  - compare identical payload, clock, reset, traffic seed, and firmware setup
  - separate headline bandwidth from useful payload bandwidth
  - explain why the protocol mechanism moved the metric

Latency distribution

LATENCY HISTOGRAM — Snoop & Cache Maintenance

count
  |               ███
  |             ███████
  |          █████████████
  |        █████████████████        <- long tail = the real complaint
  |      ████████████████████████████
  +------------------------------------> latency
   p50      p90    p95       p99  (watch p99, not the average)

Average hides the tail; product pain lives at p95/p99.

• Track cache maintenance latency, invalidation count, stale data escapes by traffic class, payload size, and clock/reset mode.

• Report p50/p95/p99 latency when user-visible stalls matter.

• Include legal maximums and product targets; they are not the same thing.

• Always store the metric next to the artifact that produced it.

Protocol deep dive

Coherence extends memory transactions with snoop and state — traffic multiplies when software shares cache lines.

Concept diagram

COHERENCE TRAFFIC FLOW

RN issues coherent read
   -> HN looks up directory
   -> snoops to sharers
   -> data + state update returned

False sharing: different variables, same cache line -> coherence storm.

Metric graph

COHERENCY TRAFFIC STACK

data fetch        ████████
snoop responses   ██████████████
writebacks        ██████
maintenance ops   ████

High snoop stack with good IPC -> suspect line sharing before faster NoC.

Metrics and artifacts to collect

• snoop rate

• intervention latency

• coherency transaction mix

• false sharing indicators

Mini case study

Benchmark IPC looked fine but system power spiked: per-core counters were on one cache line. Padding counters fixed coherency traffic without any NoC change.

Debug branches

• If snoop latency high, check home node placement and directory policy.

• If ordering bug, run litmus sequences before microarch changes.

• If traffic storm, profile cache line sharing in software layout.

Senior review question

Ask: what is the first transaction that deviates, and which spec rule does it test?

Key takeaways

• Connect every protocol claim to a transaction identity and measurable metric.

• Store the artifact (waveform, log, counter) next to every signoff decision.

Common pitfalls

• Debugging timeouts without finding the first bad transaction.

• Quoting peak bus width without payload efficiency and retry overhead.

• Treating VIP compliance as a substitute for system integration replay.

How to read the numbers

cache maintenance latency, invalidation count, stale data escapes must be split by traffic class, payload size, and reset mode.