Interface Protocols · All levels
CHI Topology Basics: Reports & Metrics
Reports & Metrics for CHI Topology Basics.
Reports and metrics
Reports & Metrics for CHI Topology Basics focuses on request retry rate, directory occupancy, p99 fabric latency. The goal is to connect the observable symptom to protocol mechanism, ownership, and regression risk.
The job of a report is to turn request retry rate, directory occupancy, p99 fabric latency 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 — request retry rate, directory occupancy, p99 fabric latency
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 metricLatency distribution
LATENCY HISTOGRAM — CHI Topology Basics
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 request retry rate, directory occupancy, p99 fabric latency 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
request retry rate, directory occupancy, p99 fabric latency must be split by traffic class, payload size, and reset mode.