MB5 PD reuse-centric ablation: tooling, data, Fig 1-3

Three-axis controlled ablation of PD-colo vs PD-disagg on synthetic regular
traces (closed-loop, controlled reuse via REPLAY_NO_REALIZED_PREFIX) on the
clean stack (e13391e gated off).

  Axis 1 (Fig 1) -- reuse 6%->94% at N=8, in8192/out256
  Axis 2 (Fig 2) -- shape in2048/out2048 -> in32768/out64 at N=8, reuse~70%
  Axis 3 (Fig 3) -- concurrency N=8/16/32/64 at reuse~71%, in8192/out256

Findings:
  * APC parity colo=PD at every reuse (5.5/22/44/66/77/82%) -- contamination
    fix validated.
  * PD edge erodes 1.57x->1.10x with reuse; prefill GPUs strand 26%->9%.
  * Shape: PD-best peaks mid-sweep (1.34x at in8192/out512); wrong PD ratio
    catastrophic at prefill extreme (in32768/out64 pd2 = 378/400, p99 432s).
  * Concurrency: PD wins N<=32 (1.23-1.29x), TIPS at N=64 -- pd2/pd4
    crater (APC 71%->1.4%, TPS -30%) while colo scales cleanly.

Infrastructure:
  * replayer: --max-inflight-sessions, --inter-turn-think, --no-realized-prefix
    (env-defaulted via REPLAY_MAX_INFLIGHT, REPLAY_INTER_TURN_THINK_S,
    REPLAY_NO_REALIZED_PREFIX).
  * mb5_run.sh: writes bench_config.json + gpu_util.csv + run_window.json +
    instance_apc.txt + metrics.jsonl for bench_report/fig_agg ingest.
  * fig_agg.py: per-arm GPU role split + producer-side APC; --json mode.
  * gpu_util_report.py: companion per-GPU util report from gpu_util.csv.
  * partial_summary.py: stats from in-flight replay_metrics.jsonl
    (works before metrics.summary.json exists).

Data: analysis/mb5_pd_ablation/fig{1,2,3}.json (24 + 20 + 16 rows).
Figures: figs/mb5_pd_ablation/fig{1_reuse,2_shape,3_concurrency}_axis.png.

microbench/fresh_setup/mb5_run.sh

@@ -69,6 +69,13 @@ run_one() {
     source "${VENV}/bin/activate"
     local replay_out="${rundir}/replay_metrics.jsonl"
     mkdir -p "$(dirname "${replay_out}")"
+    # bench_report.py inputs: worker->gpu map (worker i == gpu i for every config;
+    # for PD, workers 0-3 are producers on gpu0-3, 4-7 consumers on gpu4-7).
+    printf '{"base_port":8000,"n_instances":8,"gpu_indices":[0,1,2,3,4,5,6,7]}\n' \
+        > "${rundir}/bench_config.json"
+    # per-GPU utilization timeseries over the replay window (2s sampling)
+    bash "${SCRIPT_DIR}/gpu_monitor.sh" "${rundir}/gpu_util.csv" 2 >/dev/null 2>&1 &
+    local GPU_MON=$!
     local t0
     t0=$(date +%s.%N)
     if ! PYTHONPATH="${FRESH_ROOT}" python -m replayer \
@@ -82,6 +89,7 @@ run_one() {
         t1=$(date +%s.%N)
         local wall=$(python -c "print(${t1} - ${t0})")
         echo "[mb5-run] REPLAY FAILED after ${wall} s; see ${OUT_ROOT}/${config}_rep${rep}_replay.log"
+        kill "${GPU_MON}" 2>/dev/null || true
         bash "${LAUNCH}" stop > /dev/null 2>&1 || true
         return 1
     fi
@@ -91,6 +99,9 @@ run_one() {
     wall_clock_s=$(python -c "print(${t1} - ${t0})")
     echo "[mb5-run] replay done in ${wall_clock_s}s"
     echo "${wall_clock_s}" > "${rundir}/wall_clock_s.txt"
+    kill "${GPU_MON}" 2>/dev/null || true
+    printf '{"t_start_unix":%s,"t_end_unix":%s}\n' "${t0}" "${t1}" > "${rundir}/run_window.json"
+    cp -f "${replay_out}" "${rundir}/metrics.jsonl"   # bench_report.py expects metrics.jsonl
 
     # Per-instance prefix-cache counters, scraped from each backend BEFORE
     # teardown. For PD this is the only honest reuse signal: producer ports