Recovery, Retrain, and Hot Reset Flows: Reports and Metrics

Reports and metrics

Reports and Metrics for Recovery, Retrain, and Hot Reset Flows focuses on Recovery entry count, retrain success rate, and service disruption duration. The purpose is to turn memory observations into mechanism-backed actions with explicit owners and release-safe validation.

Reports should explain why Recovery entry count, retrain success rate, and service disruption duration moved, not simply that it moved. Require evidence that links the movement to command behavior, queue policy, PHY margin, or reliability controls.

Before/after trend

BEFORE/AFTER TREND - Recovery, Retrain, and Hot Reset Flows

metric        before    after fix
------------  --------  ---------
bandwidth     42 GB/s   48 GB/s
p99 latency   18 us     9 us
error rate    12/hr     0/hr

Evidence matrix

PCIe/CXL EVIDENCE MATRIX - Recovery, Retrain, and Hot Reset Flows

+-------------------------------+--------------------------------+--------------------------------+---------------------------+
| Evidence                      | Tells you                      | Does not prove                 | Next action               |
+-------------------------------+--------------------------------+--------------------------------+---------------------------+
| TLP type mix + credit stall counters    | protocol-layer stall cost    | link integrity and replay behavior   | inspect training margins  |
| queue age + class breakdown   | fairness and starvation risk   | command legality details       | parse command timeline    |
| LTSSM timeline + ordered set progression | timing-window pressure         | root cause by itself           | correlate with topology map|
| eye / Vref / skew snapshots   | PHY margin and drift behavior  | controller policy quality      | pair with schedule logs   |
| CE/UE + scrub telemetry       | reliability trajectory         | immediate perf bottleneck only | map to hotspot apcieesses  |
+-------------------------------+--------------------------------+--------------------------------+---------------------------+

• Track p50/p95/p99 latency and effective bandwidth together.

• Include command and queue context alongside high-level counters.

• Tag reports with firmware, timing profile, and thermal state.

• Call out contradictory evidence instead of hiding it.

PCIe/CXL deep dive

LTSSM and equalization determine whether high-speed links are stable under corner traffic and retimer paths.

Concept diagram

LTSSM + EQ

Detect -> Polling -> Config -> L0 <-> Recovery

Metric graph

LINK INSTABILITY SOURCES

EQ margin           ██████
retimer FW          ████
SI/cable plant      ███

Reports and artifacts

• LTSSM state log

• EQ coefficient dump

• negotiated speed/width snapshot

• recovery trigger timeline

Mini case study

Gen5 passed cold boot EQ but entered Recovery loops under DMA heat after retimer firmware update.

Debug branches

• Capture ordered sets at failure boundary

• Compare EQ presets across temperature corners

• Bypass retimer to isolate segment faults

Senior review question

Ask: which latency, bandwidth, and reliability evidence proves this PCIe/CXL topic is closed under real traffic?

Key takeaways

• Always tie controller and PHY counter shifts to application latency and throughput outcomes.

• Lock firmware timing profile, thermal condition, and DIMM state before comparing PCIe/CXL captures.

Common pitfalls

• Chasing peak bandwidth while ignoring p99 latency and fairness tails.

• Changing timing guardbands without separating SI noise from scheduling issues.

• Declaring closure without reliability gates, fault injection, and regression replay.

Report interpretation

Bit errors, speed changes, and power events trigger Recovery where the link re-synchronizes without full re-enumeration. Poor recovery handling drops packets, stalls DMA, and can cascade into surprise-down if timeouts are misconfigured. PCIe/CXL inefficiency is multiplicative: one extra ACTIVATE, one unnecessary turnaround, one weak lane margin, or one refresh collision repeated across billions of accesses can dominate product tail latency and power.

Use Recovery entry count, retrain success rate, and service disruption duration as the opening signal, not the conclusion. A metric move only becomes actionable when paired with workload context, command traces, training telemetry, and evidence artifacts such as Recovery trigger log, DL replay correlation, and service impact timeline.

Link training is a margin and state-machine problem spanning PHY, retimers, cables, and platform power sequencing. Senior review quality comes from proving a complete chain: request pattern -> memory-state transition -> bottleneck mechanism -> smallest owner fix -> regression-safe validation.

For Recovery, Retrain, and Hot Reset Flows, reports should explain why Recovery entry count, retrain success rate, and service disruption duration moved: fewer row misses, lower turnaround waste, better refresh placement, or stronger lane margin stability.

Strong reports include consistency checks: scheduler narrative matches command logs; PHY narrative matches margin sweeps; reliability narrative matches CE/UE trajectories.