Predictor Types and Accuracy: Reports and Metrics

Reports and metrics

Reports and Metrics for Predictor Types and Accuracy centers on global prediction accuracy, MPKI, and confidence calibration error. Tie every claim to a measurable artifact and an owner-controlled action.

Before/after trend

BEFORE / AFTER TREND - Predictor Types and Accuracy

metric quality
  ^
  |                        o target region
  |                 o post-fix rerun
  |            o
  |      o baseline (failing)
  +----------------------------------------------> iteration
      capture       isolate mechanism       close

Use this to prove improvement is causal and stable.

Root-cause tree

ROOT-CAUSE TREE - Predictor Types and Accuracy

global prediction accuracy, MPKI, and confidence calibration error regressed
        |
  reproducible on fixed seed?
      /               \
    no                 yes
    |                   |
env/tool drift      first failing stage?
                    /        |        \
                front-end   execute   memory/system
                   |          |            |
              fetch/decode   port/ROB   cache/TLB/NoC

Stop at first confirmed mechanism, then patch with owner accountability.

• Track global prediction accuracy, MPKI, and confidence calibration error on representative workloads, not only microbenchmarks.

• Always include build and runtime metadata in report headers.

• Correlate CPI stack with stage-specific traces before deciding fixes.

• Report tail latency and stability, not only mean throughput.

CPU deep dive

Speculation helps only when wrong-path cost and recovery bandwidth are tightly controlled.

Concept diagram

SPECULATION LOOP

predict direction/target -> speculative fetch/decode -> resolve -> flush/recover

Metric graph

SPECULATION COST MIX

wrong-path decode work  █████
flush recovery delay    ████
refill starvation       ███

Reports and artifacts

• branch accuracy by workload

• BTB/RAS pressure report

• mispredict recovery timeline

• bad-speculation CPI share

Mini case study

Indirect branch aliasing in one service raised wrong-path work enough to dominate total CPI despite high ALU utilization.

Debug branches

• Break down mispredicts by branch family and code region

• Measure flush depth and refill bandwidth separately

• Validate predictor changes under security mitigation settings

Senior review question

Ask: which CPI/latency evidence proves this topic is truly closed beyond synthetic benchmarks?

Key takeaways

• Always connect microarchitectural counter changes to product workload outcomes.

• Lock binary, compiler, firmware, and thermal metadata before comparing CPU traces.

Common pitfalls

• Treating average IPC as sufficient proof while ignoring latency tails and outliers.

• Applying predictor or prefetch tweaks without first-failing-stage attribution.

• Declaring closure without reproducible perf, correctness, and power gates.

Report interpretation

Local, global, hybrid, and neural-style predictors trade storage, latency, and aliasing behavior; tuning confidence and update policy determines real-world stability under changing branch patterns. CPU teams pay for repeated inefficiency: one extra bubble, one wrong target, one port conflict, or one translation miss pattern can replicate across billions of instructions and dominate product-level latency and energy.

Use global prediction accuracy, MPKI, and confidence calibration error as an investigation start point, not as the conclusion. A counter movement only becomes actionable when paired with workload phase tags, PMU event context, a controlled repro, and artifact evidence such as predictor-type comparison matrix, accuracy-by-workload plot, and confidence histogram.

Speculation quality is a control-flow economics problem: wrong-path work is expensive and must be bounded. Senior review quality comes from proving the full chain: workload request -> microarchitectural response -> measured bottleneck -> smallest owner fix -> regression-safe validation.

For Predictor Types and Accuracy, reports should explain why global prediction accuracy, MPKI, and confidence calibration error changed: more useful retire, less wrong-path work, reduced queue pressure, or better memory translation/servicing.

Strong reports include consistency checks: CPI stack narrative matches stage occupancy; branch story matches redirect logs; memory story matches miss and latency distributions.