Reports and metrics

Reports and Metrics for FR-FCFS, Row-Buffer Locality, and Page Policy Control focuses on Row-hit rate, effective command efficiency, and average activate/precharge overhead per request.. The purpose is to turn memory observations into mechanism-backed actions with explicit owners and release-safe validation.

Reports should explain why Row-hit rate, effective command efficiency, and average activate/precharge overhead per request. 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 GRAPH - FR-FCFS, Row-Buffer Locality, and Page Policy Control

metric quality
  ^
  |                       o target band
  |                o post-fix sweep
  |           o
  |      o baseline (failing)
  +----------------------------------------------> iteration
      evidence capture   fix applied   closure run

Use this view to prove improvement is causal, not accidental.

Evidence matrix

DRAM EVIDENCE MATRIX - FR-FCFS, Row-Buffer Locality, and Page Policy Control

+-------------------------------+--------------------------------+--------------------------------+---------------------------+
| Evidence                      | Tells you                      | Does not prove                 | Next action               |
+-------------------------------+--------------------------------+--------------------------------+---------------------------+
| row-hit/miss + ACT/PRE mix    | locality and row-state cost    | lane-level capture integrity   | inspect training margins  |
| queue age + class breakdown   | fairness and starvation risk   | command legality details       | parse command timeline    |
| JEDEC legality + bus timeline | timing-window pressure         | root cause by itself           | correlate with traffic 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 addresses  |
+-------------------------------+--------------------------------+--------------------------------+---------------------------+

• 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.

DRAM deep dive

Controller policy decides whether DRAM serves locality, fairness, and QoS targets simultaneously.

Concept diagram

CONTROLLER SCHEDULING LOOP

request queues -> row-policy + priority -> command issue -> bank state update

Metric graph

QUEUE PRESSURE MIX

row-hit preference bias ██████
aging/fairness pressure █████
QoS override cost       ███

Reports and artifacts

• scheduler policy comparison

• queue age distribution

• starvation/fairness incident report

• QoS latency percentile dashboard

Mini case study

FR-FCFS tuning improved bulk throughput but starved latency-critical traffic until age caps and class quotas were added.

Debug branches

• Measure queue age tails by traffic class

• Separate row-hit gains from fairness regressions

• Stress policy under mixed burst and random streams

Senior review question

Ask: which latency, bandwidth, and reliability evidence proves this DRAM 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 DRAM 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

FR-FCFS (First-Ready, First-Come-First-Serve) prioritizes commands that are timing-ready now, and among those typically prefers older arrivals; in practice this strongly favors row hits because an open-row access can issue quickly while a row miss requires PRECHARGE plus ACTIVATE latency. The policy boosts throughput by harvesting row-buffer locality, but can also bias service toward hot rows and penalize streams that repeatedly miss. Page policy selection (open-page, close-page, or adaptive hybrids) determines whether the controller keeps a row open after service or proactively closes it to reduce future conflict cost. Open-page favors bursty locality workloads, while close-page limits row-conflict penalties and can stabilize latency under random access. Adaptive implementations monitor hit/miss patterns, bank-level contention, and command bus pressure, then adjust close timing or row-retention heuristics per bank. The controller must reconcile this with timing constraints such as tRAS minimum, tFAW power windows, and bank-group turnaround rules, because aggressive row management can improve one metric while degrading global fairness or power integrity. DRAM 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 Row-hit rate, effective command efficiency, and average activate/precharge overhead per request. 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 Row-buffer analytics report: FR-FCFS issue decisions, row-hit/miss timeline, and adaptive page-policy state transitions..

Memory-controller quality is measured by throughput and tail predictability under mixed traffic, not average bandwidth alone. Senior review quality comes from proving a complete chain: request pattern -> memory-state transition -> bottleneck mechanism -> smallest owner fix -> regression-safe validation.

For FR-FCFS, Row-Buffer Locality, and Page Policy Control, reports should explain why Row-hit rate, effective command efficiency, and average activate/precharge overhead per request. 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.