PE_DMA perf: dual-peak utilisation (single-path + aggregate)

Each scenario now shows TWO bars:

  util_single    = effective_bw / single-path peak × 100
                   (peak = min bw_gbs on first issuer's path)
  util_aggregate = effective_bw / aggregate-resource peak × 100
                   (peak = max-min fair share across concurrent paths)

Aggregate peak uses a max-min fair-share computation: each concurrent
path's sustainable share on an edge is bw_gbs / usage_count, the
per-path throughput is the min share along its edges, and the aggregate
peak is the sum across paths. This produces the correct answer for both
shared-bottleneck scenarios (N paths converge on one wire → aggregate =
wire BW) and multi-lane shared resources (UCIe's 4 connections used in
parallel → aggregate ≈ 4 × per-conn BW), without enumerating max-flow.

Single-issuer (no_congestion) → util_single == util_aggregate by
definition. Congestion exposes the divergence:
  ctrl_hot_{1,2,3}, all_pe_to_pe0 → both metrics agree (one shared
                    bottleneck: r0c0→hbm_ctrl.pe0 @ 256 GB/s)
  8×PE eastbound → util_single=106 % (single conn @ 128 GB/s) but
                    util_aggregate=85 % (UCIe-W.conn0 @ 7-way shared,
                    aggregate peak ≈ 160 GB/s under the current
                    cross-cube routing that funnels via cube1.r0c0).

Verification updated to assert:
  (2) util_aggregate ≤ 100 % (effective BW can't exceed the aggregate
      resource peak, by construction).
  (3) single-issuer util_single == util_aggregate.
  (7) ucie_eastbound: util_aggregate is meaningfully smaller than
      util_single (the multi-lane peak correction is observable).

CSV grows with peak_aggregate_bw_gbs and util_aggregate_pct columns;
breakdown columns retained.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

docs/diagrams/pe_dma_perf/summary.csv

@@ -1,24 +1,24 @@
-graph,scenario,label,nbytes,n_issuers,total_ns,makespan_ns,min_lat_ns,bottleneck_bw_gbs,effective_bw_gbs,util_pct,pe_setup,noc_mesh,ucie,fabric,streaming,hbm_ctrl,contention,path,first_path
+graph,scenario,label,nbytes,n_issuers,total_ns,makespan_ns,min_lat_ns,peak_single_bw_gbs,peak_aggregate_bw_gbs,effective_bw_gbs,util_single_pct,util_aggregate_pct,pe_setup,noc_mesh,ucie,fabric,streaming,hbm_ctrl,contention,path,first_path
 no_congestion,local,"SAME_CUBE
-PE_LOCAL",16384,1,77.0,,,256.0,212.7792207792208,83.11688311688312,1.0,2.0,0.0,0.0,63.0,9.0,2.0,pe0.pe_dma -> cube0.r0c0 -> hbm_ctrl.pe0,
+PE_LOCAL",16384,1,77.0,,,256.0,256.0,212.7792207792208,83.11688311688312,83.11688311688312,1.0,2.0,0.0,0.0,63.0,9.0,2.0,pe0.pe_dma -> cube0.r0c0 -> hbm_ctrl.pe0,
 no_congestion,same_cube_best,"SAME_CUBE
 REMOTE_BEST
-(pe0→pe1)",16384,1,82.06,,,256.0,199.6587862539605,77.99171338045332,1.0,5.03,0.0,0.0,63.0,9.0,4.030000000000001,pe0.pe_dma -> cube0.r0c0 -> cube0.r0c1 -> hbm_ctrl.pe1,
+(pe0→pe1)",16384,1,82.06,,,256.0,256.0,199.6587862539605,77.99171338045332,77.99171338045332,1.0,5.03,0.0,0.0,63.0,9.0,4.030000000000001,pe0.pe_dma -> cube0.r0c0 -> cube0.r0c1 -> hbm_ctrl.pe1,
 no_congestion,same_cube_worst,"SAME_CUBE
 REMOTE_WORST
-(pe0→pe7)",16384,1,117.50000000000001,,,256.0,139.4382978723404,54.46808510638297,1.0,26.25,0.0,0.0,63.0,9.0,18.250000000000014,pe0.pe_dma -> cube0.r0c0 -> cube0.r1c0 -> cube0.r1c1 -> cube0.r1c2 -> cube0.r1c3 -> cube0.r4c3 -> cube0.r4c4 -> cube0.r5c4 -> cube0.r5c5 -> hbm_ctrl.pe7,
+(pe0→pe7)",16384,1,117.50000000000001,,,256.0,256.0,139.4382978723404,54.46808510638297,54.46808510638297,1.0,26.25,0.0,0.0,63.0,9.0,18.250000000000014,pe0.pe_dma -> cube0.r0c0 -> cube0.r1c0 -> cube0.r1c1 -> cube0.r1c2 -> cube0.r1c3 -> cube0.r4c3 -> cube0.r4c4 -> cube0.r5c4 -> cube0.r5c5 -> hbm_ctrl.pe7,
 no_congestion,remote_cube_best,"REMOTE_CUBE
 REMOTE_BEST
-(cube0→cube1)",16384,1,202.51999999999998,,,128.0,80.90065178747778,63.20363420896702,1.0,6.0,32.510000000000005,0.0,126.0,9.0,28.00999999999999,pe0.pe_dma -> cube0.r0c0 -> ucie-N.conn0 -> cube0.ucie-N -> ucie-N.conn3 -> cube0.r0c5 -> ucie-E.conn0 -> cube0.ucie-E -> cube1.ucie-W -> ucie-W.conn0 -> cube1.r0c0 -> hbm_ctrl.pe0,
+(cube0→cube1)",16384,1,202.51999999999998,,,128.0,128.0,80.90065178747778,63.20363420896702,63.20363420896702,1.0,6.0,32.510000000000005,0.0,126.0,9.0,28.00999999999999,pe0.pe_dma -> cube0.r0c0 -> ucie-N.conn0 -> cube0.ucie-N -> ucie-N.conn3 -> cube0.r0c5 -> ucie-E.conn0 -> cube0.ucie-E -> cube1.ucie-W -> ucie-W.conn0 -> cube1.r0c0 -> hbm_ctrl.pe0,
 no_congestion,remote_cube_worst,"REMOTE_CUBE
 REMOTE_WORST
-(cube0→cube15.pe7)",16384,1,573.1199999999999,,,128.0,28.587381351200452,22.333891680625353,1.0,30.0,219.05999999999995,0.0,126.0,9.0,188.05999999999995,pe0.pe_dma -> cube0.r0c0 -> ucie-N.conn0 -> cube0.ucie-N -> ucie-N.conn3 -> cube0.r0c5 -> ucie-E.conn0 -> cube0.ucie-E -> cube1.ucie-W -> ucie-W.conn0 -> cube1.r0c0 -> ucie-N.conn0 -> cube1.ucie-N -> ucie-N.conn3 -> cube1.r0c5 -> ucie-E.conn0 -> cube1.ucie-E -> cube2.ucie-W -> ucie-W.conn0 -> cube2.r0c0 -> ucie-N.conn0 -> cube2.ucie-N -> ucie-N.conn3 -> cube2.r0c5 -> ucie-E.conn0 -> cube2.ucie-E -> cube3.ucie-W -> ucie-W.conn0 -> cube3.r0c0 -> ucie-N.conn0 -> cube3.ucie-N -> ucie-N.conn3 -> cube3.r0c5 -> ucie-E.conn0 -> cube3.ucie-E -> ucie-E.conn3 -> cube3.r5c5 -> ucie-S.conn3 -> cube3.ucie-S -> cube7.ucie-N -> ucie-N.conn3 -> cube7.r0c5 -> ucie-E.conn0 -> cube7.ucie-E -> ucie-E.conn3 -> cube7.r5c5 -> ucie-S.conn3 -> cube7.ucie-S -> cube11.ucie-N -> ucie-N.conn3 -> cube11.r0c5 -> ucie-E.conn0 -> cube11.ucie-E -> ucie-E.conn3 -> cube11.r5c5 -> ucie-S.conn3 -> cube11.ucie-S -> cube15.ucie-N -> ucie-N.conn3 -> cube15.r0c5 -> ucie-E.conn0 -> cube15.ucie-E -> ucie-E.conn3 -> cube15.r5c5 -> hbm_ctrl.pe7,
+(cube0→cube15.pe7)",16384,1,573.1199999999999,,,128.0,128.0,28.587381351200452,22.333891680625353,22.333891680625353,1.0,30.0,219.05999999999995,0.0,126.0,9.0,188.05999999999995,pe0.pe_dma -> cube0.r0c0 -> ucie-N.conn0 -> cube0.ucie-N -> ucie-N.conn3 -> cube0.r0c5 -> ucie-E.conn0 -> cube0.ucie-E -> cube1.ucie-W -> ucie-W.conn0 -> cube1.r0c0 -> ucie-N.conn0 -> cube1.ucie-N -> ucie-N.conn3 -> cube1.r0c5 -> ucie-E.conn0 -> cube1.ucie-E -> cube2.ucie-W -> ucie-W.conn0 -> cube2.r0c0 -> ucie-N.conn0 -> cube2.ucie-N -> ucie-N.conn3 -> cube2.r0c5 -> ucie-E.conn0 -> cube2.ucie-E -> cube3.ucie-W -> ucie-W.conn0 -> cube3.r0c0 -> ucie-N.conn0 -> cube3.ucie-N -> ucie-N.conn3 -> cube3.r0c5 -> ucie-E.conn0 -> cube3.ucie-E -> ucie-E.conn3 -> cube3.r5c5 -> ucie-S.conn3 -> cube3.ucie-S -> cube7.ucie-N -> ucie-N.conn3 -> cube7.r0c5 -> ucie-E.conn0 -> cube7.ucie-E -> ucie-E.conn3 -> cube7.r5c5 -> ucie-S.conn3 -> cube7.ucie-S -> cube11.ucie-N -> ucie-N.conn3 -> cube11.r0c5 -> ucie-E.conn0 -> cube11.ucie-E -> ucie-E.conn3 -> cube11.r5c5 -> ucie-S.conn3 -> cube11.ucie-S -> cube15.ucie-N -> ucie-N.conn3 -> cube15.r0c5 -> ucie-E.conn0 -> cube15.ucie-E -> ucie-E.conn3 -> cube15.r5c5 -> hbm_ctrl.pe7,
 no_congestion,remote_sip,"REMOTE_SIP
 SAME_CUBE_SAME_PE
-(sip0→sip1)",16384,1,408.5216666666663,,,128.0,40.10558395515541,31.332487464965165,1.0,4.0,37.040000000000006,22.09666666666667,126.0,9.0,209.38499999999962,pe0.pe_dma -> cube0.r0c0 -> ucie-N.conn0 -> cube0.ucie-N -> io0.ucie-P0 -> ucie-P0.conn0 -> io0.noc -> io0.pcie_ep -> fabric.switch0 -> io0.pcie_ep -> io0.noc -> ucie-P0.conn0 -> io0.ucie-P0 -> cube0.ucie-N -> ucie-N.conn0 -> cube0.r0c0 -> hbm_ctrl.pe0,
-congestion,ctrl_hot_1,1×PE → pe0_slice,16384,1,,82.06,82.06,256.0,199.6587862539605,77.99171338045332,1.0,5.03,0.0,0.0,63.0,9.0,4.030000000000001,,pe1.pe_dma -> cube0.r0c1 -> cube0.r0c0 -> hbm_ctrl.pe0
-congestion,ctrl_hot_2,2×PE → pe0_slice,16384,2,,158.3450000000001,134.2400000000001,256.0,206.94054122327813,80.83614891534302,1.0,5.03,0.0,0.0,63.0,9.0,80.31500000000011,,pe1.pe_dma -> cube0.r0c1 -> cube0.r0c0 -> hbm_ctrl.pe0
-congestion,ctrl_hot_3,3×PE → pe0_slice,16384,3,,230.0750000000001,139.94000000000008,256.0,213.6346843420623,83.45104857111808,1.0,5.03,0.0,0.0,63.0,9.0,152.0450000000001,,pe1.pe_dma -> cube0.r0c1 -> cube0.r0c0 -> hbm_ctrl.pe0
+(sip0→sip1)",16384,1,408.5216666666663,,,128.0,128.0,40.10558395515541,31.332487464965165,31.332487464965165,1.0,4.0,37.040000000000006,22.09666666666667,126.0,9.0,209.38499999999962,pe0.pe_dma -> cube0.r0c0 -> ucie-N.conn0 -> cube0.ucie-N -> io0.ucie-P0 -> ucie-P0.conn0 -> io0.noc -> io0.pcie_ep -> fabric.switch0 -> io0.pcie_ep -> io0.noc -> ucie-P0.conn0 -> io0.ucie-P0 -> cube0.ucie-N -> ucie-N.conn0 -> cube0.r0c0 -> hbm_ctrl.pe0,
+congestion,ctrl_hot_1,1×PE → pe0_slice,16384,1,,82.06,82.06,256.0,256.0,199.6587862539605,77.99171338045332,77.99171338045332,1.0,5.03,0.0,0.0,63.0,9.0,4.030000000000001,,pe1.pe_dma -> cube0.r0c1 -> cube0.r0c0 -> hbm_ctrl.pe0
+congestion,ctrl_hot_2,2×PE → pe0_slice,16384,2,,158.3450000000001,134.2400000000001,256.0,256.0,206.94054122327813,80.83614891534302,80.83614891534302,1.0,5.03,0.0,0.0,63.0,9.0,80.31500000000011,,pe1.pe_dma -> cube0.r0c1 -> cube0.r0c0 -> hbm_ctrl.pe0
+congestion,ctrl_hot_3,3×PE → pe0_slice,16384,3,,230.0750000000001,139.94000000000008,256.0,256.0,213.6346843420623,83.45104857111808,83.45104857111808,1.0,5.03,0.0,0.0,63.0,9.0,152.0450000000001,,pe1.pe_dma -> cube0.r0c1 -> cube0.r0c0 -> hbm_ctrl.pe0
 congestion,ucie_eastbound,"8×PE corresp.
-cube0→cube1",16384,8,,962.52,438.52,128.0,136.17587167019906,106.387399742343,1.0,6.0,32.510000000000005,0.0,126.0,9.0,788.01,,pe0.pe_dma -> cube0.r0c0 -> ucie-N.conn0 -> cube0.ucie-N -> ucie-N.conn3 -> cube0.r0c5 -> ucie-E.conn0 -> cube0.ucie-E -> cube1.ucie-W -> ucie-W.conn0 -> cube1.r0c0 -> hbm_ctrl.pe0
-congestion,all_pe_to_pe0,8×PE → pe0_slice,16384,8,,558.2499999999998,195.0,256.0,234.7908643081058,91.71518137035383,1.0,2.0,0.0,0.0,63.0,9.0,483.2499999999998,,pe0.pe_dma -> cube0.r0c0 -> hbm_ctrl.pe0
+cube0→cube1",16384,8,,962.52,438.52,128.0,159.99999999999997,136.17587167019906,106.387399742343,85.10991979387443,1.0,6.0,32.510000000000005,0.0,126.0,9.0,788.01,,pe0.pe_dma -> cube0.r0c0 -> ucie-N.conn0 -> cube0.ucie-N -> ucie-N.conn3 -> cube0.r0c5 -> ucie-E.conn0 -> cube0.ucie-E -> cube1.ucie-W -> ucie-W.conn0 -> cube1.r0c0 -> hbm_ctrl.pe0
+congestion,all_pe_to_pe0,8×PE → pe0_slice,16384,8,,558.2499999999998,195.0,256.0,256.0,234.7908643081058,91.71518137035383,91.71518137035383,1.0,2.0,0.0,0.0,63.0,9.0,483.2499999999998,,pe0.pe_dma -> cube0.r0c0 -> hbm_ctrl.pe0

scripts/plot_pe_dma_perf.py

@@ -136,6 +136,48 @@ def _bottleneck_bw(path: list[str], edge_map: dict) -> float | None:
     return min(bws) if bws else None
 
 
+def _aggregate_peak_bw(paths: list[list[str]], edge_map: dict) -> float:
+    """Max-min fair-share aggregate throughput across concurrent paths.
+
+    Each path is one unit of demand from source to destination. For each
+    edge, fair share per path = ``bw_gbs / usage_count``. A path's
+    sustainable throughput is the minimum fair share along its edges,
+    and the aggregate peak is the sum across paths. This produces the
+    correct answer for both shared-bottleneck scenarios (all paths
+    converge on one wire → aggregate = wire BW) and multi-lane shared
+    resources (UCIe's 4 connections used in parallel → aggregate = 4 ×
+    per-conn BW), without enumerating max-flow explicitly.
+
+    Examples:
+      * 3 paths sharing r0c0→hbm_ctrl.pe0 @ 256 GB/s
+          per-path = 256/3 ≈ 85.3, aggregate = 3 × 85.3 = 256 GB/s ✓
+      * 8 paths sharing 4 UCIe conns @ 128 GB/s (2 paths per conn)
+          per-path = 128/2 = 64, aggregate = 8 × 64 = 512 GB/s ✓
+      * 1 path through 256 GB/s bottleneck
+          per-path = 256, aggregate = 256 GB/s ✓ (= single-path peak)
+    """
+    from collections import Counter
+
+    edge_usage: Counter = Counter()
+    for path in paths:
+        for i in range(len(path) - 1):
+            edge_usage[(path[i], path[i + 1])] += 1
+
+    aggregate = 0.0
+    for path in paths:
+        per_path = float("inf")
+        for i in range(len(path) - 1):
+            key = (path[i], path[i + 1])
+            e = edge_map.get(key)
+            if e and e.bw_gbs:
+                share = e.bw_gbs / edge_usage[key]
+                if share < per_path:
+                    per_path = share
+        if per_path != float("inf"):
+            aggregate += per_path
+    return aggregate
+
+
 def _path_breakdown(
     path: list[str], nbytes: int, graph, edge_map, ns_per_mm: float,
 ) -> dict[str, float]:
@@ -257,9 +299,11 @@ def _run_no_congestion(nbytes: int):
         br = _path_breakdown(path, nbytes, graph, edge_map, ns_per_mm)
         formula_sum = sum(br.values())
         br["contention"] = max(0.0, total_ns - formula_sum)
-        peak_bw = _bottleneck_bw(path, edge_map) or 0.0
+        peak_single = _bottleneck_bw(path, edge_map) or 0.0
+        peak_aggregate = _aggregate_peak_bw([path], edge_map)
         eff_bw = nbytes / total_ns if total_ns > 0 else 0.0
-        util = (eff_bw / peak_bw * 100.0) if peak_bw > 0 else 0.0
+        util_single = (eff_bw / peak_single * 100.0) if peak_single > 0 else 0.0
+        util_aggregate = (eff_bw / peak_aggregate * 100.0) if peak_aggregate > 0 else 0.0
         rows.append({
             "graph": "no_congestion",
             "scenario": scn.name,
@@ -268,9 +312,11 @@ def _run_no_congestion(nbytes: int):
             "n_issuers": 1,
             "path": " -> ".join(_short_path(path)),
             "total_ns": total_ns,
-            "bottleneck_bw_gbs": peak_bw,
+            "peak_single_bw_gbs": peak_single,
+            "peak_aggregate_bw_gbs": peak_aggregate,
             "effective_bw_gbs": eff_bw,
-            "util_pct": util,
+            "util_single_pct": util_single,
+            "util_aggregate_pct": util_aggregate,
             **{c: br.get(c, 0.0) for c, _ in CATEGORIES},
         })
     return rows
@@ -333,7 +379,7 @@ def _run_congestion(nbytes: int):
     for scn in _congestion_scenarios():
         engine = GraphEngine(load_topology(TOPOLOGY_PATH))
         handles = []
-        first_path = None
+        paths: list[list[str]] = []
         for i, (ss, sc, sp, ds, dc, dp) in enumerate(scn.issues):
             pa = _hbm_pa(sip=ds, cube=dc, pe_id=dp,
                          offset=0x1000 + i * 0x100, slice_bytes=slice_bytes)
@@ -343,10 +389,10 @@ def _run_congestion(nbytes: int):
                 dst_pa=pa, nbytes=nbytes,
             )
             handles.append(engine.submit(msg))
-            if first_path is None:
             dst_node = engine._resolver.resolve(PhysAddr.decode(pa))
-                first_path = engine._router.find_path(
-                    f"sip{ss}.cube{sc}.pe{sp}", dst_node)
+            paths.append(engine._router.find_path(
+                f"sip{ss}.cube{sc}.pe{sp}", dst_node))
+        first_path = paths[0] if paths else []
         for h in handles:
             engine.wait(h)
         latencies = [engine.get_completion(h)[1]["total_ns"] for h in handles]
@@ -354,25 +400,29 @@ def _run_congestion(nbytes: int):
 
         # Breakdown uses the first issuer's path as a representative;
         # ``contention`` absorbs serialization across requests.
-        br = _path_breakdown(first_path or [], nbytes, graph, edge_map, ns_per_mm)
+        br = _path_breakdown(first_path, nbytes, graph, edge_map, ns_per_mm)
         formula_sum = sum(br.values())
         br["contention"] = max(0.0, makespan - formula_sum)
-        peak_bw = (_bottleneck_bw(first_path or [], edge_map) or 0.0)
+        peak_single = _bottleneck_bw(first_path, edge_map) or 0.0
+        peak_aggregate = _aggregate_peak_bw(paths, edge_map)
         total_bytes = nbytes * len(scn.issues)
         eff_bw = total_bytes / makespan if makespan > 0 else 0.0
-        util = (eff_bw / peak_bw * 100.0) if peak_bw > 0 else 0.0
+        util_single = (eff_bw / peak_single * 100.0) if peak_single > 0 else 0.0
+        util_aggregate = (eff_bw / peak_aggregate * 100.0) if peak_aggregate > 0 else 0.0
         rows.append({
             "graph": "congestion",
             "scenario": scn.name,
             "label": scn.label,
             "nbytes": nbytes,
             "n_issuers": len(scn.issues),
-            "first_path": " -> ".join(_short_path(first_path or [])),
+            "first_path": " -> ".join(_short_path(first_path)),
             "makespan_ns": makespan,
             "min_lat_ns": min(latencies) if latencies else 0.0,
-            "bottleneck_bw_gbs": peak_bw,
+            "peak_single_bw_gbs": peak_single,
+            "peak_aggregate_bw_gbs": peak_aggregate,
             "effective_bw_gbs": eff_bw,
-            "util_pct": util,
+            "util_single_pct": util_single,
+            "util_aggregate_pct": util_aggregate,
             **{c: br.get(c, 0.0) for c, _ in CATEGORIES},
         })
     return rows
@@ -386,39 +436,60 @@ def _short_path(path: Iterable[str]) -> list[str]:
 
 
 def _plot_bw_utilization(rows, title, out_path):
-    """Plot Effective BW utilization (%) per scenario.
+    """Plot Effective BW utilization (%) per scenario with TWO bars:
 
-    Each bar is util_pct = effective_bw / peak_bottleneck_bw × 100.
-    Annotation shows effective and peak in GB/s. A horizontal dashed
-    line marks 100 % (single-path peak); bars exceeding it indicate
-    the scenario uses multiple parallel resources (e.g. UCIe's 4
-    connections) beyond the bottleneck of any single path.
+      util_single    = effective_bw / single-path peak × 100
+      util_aggregate = effective_bw / aggregate-resource peak × 100
+
+    The aggregate peak sums the BW of *distinct* bottleneck edges across
+    all issuer paths — modelling multi-lane shared resources (e.g. UCIe's
+    4 connections) correctly. For scenarios where all paths share one
+    bottleneck wire the two peaks are equal and the bars match.
+
+    The dashed line at 100 % is the saturation reference for both
+    metrics. util_single can exceed 100 % when multi-lane resources are
+    used; util_aggregate is bounded by 100 % by construction (since the
+    aggregate peak is the upper bound on aggregate throughput).
     """
+    import numpy as np
+
     n = len(rows)
     labels = [r["label"] for r in rows]
-    util = [r.get("util_pct", 0.0) for r in rows]
+    util_s = [r.get("util_single_pct", 0.0) for r in rows]
+    util_a = [r.get("util_aggregate_pct", 0.0) for r in rows]
     eff = [r.get("effective_bw_gbs", 0.0) for r in rows]
-    peak = [r.get("bottleneck_bw_gbs", 0.0) for r in rows]
+    peak_s = [r.get("peak_single_bw_gbs", 0.0) for r in rows]
+    peak_a = [r.get("peak_aggregate_bw_gbs", 0.0) for r in rows]
 
-    fig, ax = plt.subplots(figsize=(max(8, n * 1.4), 5.5))
-    # Colour bars by utilization band for quick scanning.
-    colours = ["#10b981" if u >= 70 else "#f59e0b" if u >= 40 else "#ef4444"
-               for u in util]
-    ax.bar(labels, util, color=colours, edgecolor="white", linewidth=0.5)
+    fig, ax = plt.subplots(figsize=(max(9, n * 1.6), 6.0))
+    x = np.arange(n)
+    w = 0.38
+    ax.bar(x - w / 2, util_s, w, color="#6366f1",
+           edgecolor="white", linewidth=0.5,
+           label="util vs single-path peak")
+    ax.bar(x + w / 2, util_a, w, color="#10b981",
+           edgecolor="white", linewidth=0.5,
+           label="util vs aggregate-resource peak")
     ax.axhline(100.0, color="grey", linestyle="--", linewidth=0.8,
-               label="single-path peak")
+               label="saturation (100 %)")
 
-    # Annotate each bar with util%, effective, and peak.
-    y_max = max(util + [100.0]) * 1.2
-    for i, (u, e, p) in enumerate(zip(util, eff, peak)):
-        ax.text(i, u + y_max * 0.012,
-                f"{u:.1f}%\n{e:.0f} / {p:.0f} GB/s",
-                ha="center", va="bottom", fontsize=8)
+    y_max = max(util_s + util_a + [100.0]) * 1.30
+    for i in range(n):
+        ax.text(i - w / 2, util_s[i] + y_max * 0.012,
+                f"{util_s[i]:.0f}%\n/{peak_s[i]:.0f}",
+                ha="center", va="bottom", fontsize=7)
+        ax.text(i + w / 2, util_a[i] + y_max * 0.012,
+                f"{util_a[i]:.0f}%\n/{peak_a[i]:.0f}",
+                ha="center", va="bottom", fontsize=7)
+        # Effective BW annotation underneath each pair
+        ax.text(i, -y_max * 0.04, f"eff={eff[i]:.0f} GB/s",
+                ha="center", va="top", fontsize=7, color="#444444")
 
+    ax.set_xticks(x)
+    ax.set_xticklabels(labels, fontsize=8)
     ax.set_ylabel("Effective BW utilization (%)")
     ax.set_title(title)
-    ax.set_ylim(0, y_max)
-    ax.tick_params(axis="x", labelsize=8)
+    ax.set_ylim(-y_max * 0.10, y_max)
     ax.legend(loc="upper right", fontsize=9, frameon=False)
     fig.tight_layout()
     fig.savefig(out_path, dpi=150)
@@ -432,7 +503,8 @@ def _write_csv(no_cong_rows, cong_rows, out_path):
     fields = [
         "graph", "scenario", "label", "nbytes", "n_issuers",
         "total_ns", "makespan_ns", "min_lat_ns",
-        "bottleneck_bw_gbs", "effective_bw_gbs", "util_pct",
+        "peak_single_bw_gbs", "peak_aggregate_bw_gbs", "effective_bw_gbs",
+        "util_single_pct", "util_aggregate_pct",
         "pe_setup", "noc_mesh", "ucie", "fabric", "streaming",
         "hbm_ctrl", "contention",
         "path", "first_path",
@@ -485,26 +557,37 @@ def _verify(rows_no_cong, rows_cong) -> list[str]:
             )
         prev_bw = min(prev_bw, by_name.get(n, {}).get("effective_bw_gbs", prev_bw))
 
-    # (2) Utilisation in (0, 250 %]; values > 100 only allowed on shared
-    # multi-lane resources (UCIe per_conn × 4 → 4-fold parallelism).
+    # (2) util_single in (0, 250 %]; util_aggregate in (0, 100 + ε %]
     for r in rows_no_cong + rows_cong:
-        u = r.get("util_pct", 0.0)
-        if u <= 0:
-            issues.append(f"{r['scenario']}: non-positive util_pct={u}")
-        if u > 250:
+        us = r.get("util_single_pct", 0.0)
+        ua = r.get("util_aggregate_pct", 0.0)
+        if us <= 0 or ua <= 0:
+            issues.append(f"{r['scenario']}: non-positive util "
+                          f"(single={us}, agg={ua})")
+        if us > 250:
             issues.append(
-                f"{r['scenario']}: util_pct={u:.1f}% exceeds 250 % — "
-                f"likely a peak-BW or effective-BW miscompute"
+                f"{r['scenario']}: util_single={us:.1f}% > 250 % — "
+                f"likely a peak or effective BW miscompute"
+            )
+        if ua > 100.0 + 1.0:  # 1 % numerical slack
+            issues.append(
+                f"{r['scenario']}: util_aggregate={ua:.1f}% > 100 % — "
+                f"effective BW must not exceed the aggregate resource peak"
             )
 
-    # (3) Single-issuer utilisation cannot exceed 100 %.
+    # (3) Single-issuer utilisation (both metrics) cannot exceed 100 %.
     for r in rows_no_cong:
-        u = r.get("util_pct", 0.0)
-        if u > 100.0 + 1e-3:
+        us = r.get("util_single_pct", 0.0)
+        ua = r.get("util_aggregate_pct", 0.0)
+        if us > 100.0 + 1e-3:
             issues.append(
-                f"no_congestion {r['scenario']}: util_pct={u:.1f}% > 100% "
-                f"for single-issuer scenario (eff={r['effective_bw_gbs']:.1f}, "
-                f"peak={r['bottleneck_bw_gbs']:.1f})"
+                f"no_congestion {r['scenario']}: util_single={us:.1f}% > 100% "
+                f"for a single-issuer scenario"
+            )
+        if abs(us - ua) > 1e-3:
+            issues.append(
+                f"no_congestion {r['scenario']}: util_single ({us:.1f}) != "
+                f"util_aggregate ({ua:.1f}) — should match for single issuer"
             )
 
     # (4) Effective BW for a single request = nbytes / total_ns
@@ -518,7 +601,7 @@ def _verify(rows_no_cong, rows_cong) -> list[str]:
             )
 
     # (5) Congestion aggregate BW grows monotonically with issuer count on
-    # the hot-target series (same shared bottleneck, more bytes / same peak).
+    # the hot-target series.
     seq = ["ctrl_hot_1", "ctrl_hot_2", "ctrl_hot_3"]
     last = 0.0
     for n in seq:
@@ -529,17 +612,27 @@ def _verify(rows_no_cong, rows_cong) -> list[str]:
             )
         last = max(last, cong_map.get(n, {}).get("effective_bw_gbs", last))
 
-    # (6) all_pe_to_pe0 must approach single-path peak (≥ 70 % util) —
-    # the shared r0c0 → hbm_ctrl.pe0 bottleneck is fully amortised when
-    # all 8 PEs target it.
+    # (6) all_pe_to_pe0 must approach the shared single-path peak.
     if "all_pe_to_pe0" in cong_map:
-        u = cong_map["all_pe_to_pe0"]["util_pct"]
+        u = cong_map["all_pe_to_pe0"]["util_single_pct"]
         if u < 70.0:
             issues.append(
-                f"congestion all_pe_to_pe0: util_pct={u:.1f}% < 70 % — "
+                f"congestion all_pe_to_pe0: util_single={u:.1f}% < 70 % — "
                 f"8-PE hotspot should saturate the shared HBM CTRL path"
             )
 
+    # (7) ucie_eastbound: util_aggregate should be meaningfully smaller
+    # than util_single (the multi-lane peak should pull the bar down).
+    if "ucie_eastbound" in cong_map:
+        e = cong_map["ucie_eastbound"]
+        if e["util_aggregate_pct"] >= e["util_single_pct"] - 5.0:
+            issues.append(
+                f"congestion ucie_eastbound: util_aggregate "
+                f"({e['util_aggregate_pct']:.1f}%) should be << "
+                f"util_single ({e['util_single_pct']:.1f}%) when UCIe's "
+                f"multi-lane peak applies"
+            )
+
     return issues
 
 
@@ -558,15 +651,21 @@ def main(nbytes: int = DEFAULT_NBYTES) -> int:
     print("\n-- No-congestion summary --")
     for r in no_cong:
         print(f"  {r['scenario']:22s}  total={r['total_ns']:7.1f} ns  "
-              f"eff={r['effective_bw_gbs']:6.1f}  peak={r['bottleneck_bw_gbs']:6.1f} "
-              f"GB/s  util={r['util_pct']:5.1f}%")
+              f"eff={r['effective_bw_gbs']:6.1f} GB/s  "
+              f"peak_s={r['peak_single_bw_gbs']:6.1f}  "
+              f"peak_a={r['peak_aggregate_bw_gbs']:6.1f}  "
+              f"util_s={r['util_single_pct']:5.1f}%  "
+              f"util_a={r['util_aggregate_pct']:5.1f}%")
     print("\n-- Congestion summary --")
     for r in cong:
         agg_bytes = r["nbytes"] * r["n_issuers"]
         print(f"  {r['scenario']:22s}  makespan={r['makespan_ns']:7.1f} ns  "
               f"agg_bytes={agg_bytes:>7d}  "
-              f"eff={r['effective_bw_gbs']:6.1f}  peak={r['bottleneck_bw_gbs']:6.1f} "
-              f"GB/s  util={r['util_pct']:5.1f}%")
+              f"eff={r['effective_bw_gbs']:6.1f} GB/s  "
+              f"peak_s={r['peak_single_bw_gbs']:6.1f}  "
+              f"peak_a={r['peak_aggregate_bw_gbs']:6.1f}  "
+              f"util_s={r['util_single_pct']:5.1f}%  "
+              f"util_a={r['util_aggregate_pct']:5.1f}%")
 
     issues = _verify(no_cong, cong)
     print("\n-- Self-verification --")