Rectangular SIP topology + 6-device allreduce sweep

mesh_2d, torus_2d, and mesh_2d_no_wrap accept optional w,h kwargs;
sqrt fall-back preserved for square layouts (back-compat tests
confirm 4-SIP and 9-SIP square configs still work). sfr_config
reads system.sips.w/h from spec and threads dims through to the
topology fn.

test_allreduce_multidevice CONFIGS switched from 4 SIPs (square)
to 6 SIPs: ring_1d_6sip, torus_2d_6sip_2x3, mesh_2d_no_wrap_6sip_2x3.
_write_temp_configs writes system.sips.w/h when supplied;
_sip_topo_dims reads them back. Latency sweep loop also moved to
6-SIP layouts. Linear-scale plot variants dropped -- only log-scale
*.png + summary.csv emitted. Plots in tests/allreduce_latency_plots
regenerated.

New tests/test_sip_topology_rectangular.py asserts neighbor
correctness for 2x3 layouts and back-compat for square fallback.

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

docs/adr/ADR-0009-kernel-execution-messaging.md

@@ -86,11 +86,36 @@ Mechanism.
 
 - `KernelLaunchMsg` carries an optional `target_start_ns: float | None`.
 - **IO_CPU** is the canonical stamper. On fan-out to M_CPUs, it
-  computes `target_start_ns = env.now + max_latency` where `max_latency`
-  is the maximum `ComponentContext.compute_path_latency_ns(path)` across
-  every target (sip, cube, pe) tuple — `path = find_node_path(io_cpu,
-  pe_cpu_id)`. The stamped value is placed on the request carried by
-  every fanned-out sub-Transaction.
+  computes `target_start_ns = env.now + max_latency` where
+  `max_latency` is the maximum, over every target (sip, cube, pe)
+  tuple, of the **two-leg dispatch chain**:
+
+  ```
+  max_latency(sip, cube, pe) =
+      compute_path_latency_ns(find_node_path(io_cpu, m_cpu(sip, cube)))
+    + compute_path_latency_ns(find_node_path(m_cpu(sip, cube), pe_cpu))
+    - io_cpu.overhead_ns
+    - m_cpu.overhead_ns
+  ```
+
+  This models the actual dispatch as **two sequential Transactions**
+  (IO_CPU → M_CPU, then M_CPU → PE_CPU). Each leg's
+  `compute_path_latency_ns` adds its endpoints' `overhead_ns`;
+  `io_cpu.overhead_ns` is subtracted because IO_CPU has already
+  paid it before this method runs, and `m_cpu.overhead_ns` is
+  subtracted once because it appears as endpoint of leg1 *and*
+  start of leg2 but is paid only once at run time. A single
+  `find_node_path(io_cpu, pe_cpu)` walk is **not** equivalent —
+  it can pick a graph path that bypasses M_CPU and silently
+  under-shoots the prediction for far cubes, breaking the D5
+  invariant.
+
+  The fanned-out sub-Transactions carry **`nbytes = 0`** for
+  `KernelLaunchMsg` (control message only). Without this,
+  large kernel-launch payloads would occupy fabric BW on the
+  shared first hop and serialize the per-cube dispatch, pushing
+  far M_CPUs past `target_start_ns` and re-introducing the
+  late-arrival violation.
 - **M_CPU** passes an already-stamped `target_start_ns` through
   unchanged. Only when the value is absent (e.g. a direct
   launch-to-M_CPU unit test) does M_CPU compute a per-cube barrier

docs/adr/ADR-0023-ipcq-pe-collective.en.md

@@ -372,24 +372,41 @@ When the receiver frees a slot, the sender must learn about it
 travel through general vc_comm fabric — it uses a **separate fast
 path**, an abstraction of the NVLink / UCIe credit-return wire.
 
-**Latency** is computed from the **bottleneck BW on the path**, not a
-magic constant:
+**Latency** is computed from the **full path latency** (per-node
+overhead + edge propagation + drain), not a magic constant:
 
 ```
 credit_size_bytes = 16  (ccl.yaml: ipcq_credit_size_bytes)
-path = router.find_path(self_pe, peer_pe)
-latency = compute_drain_ns(path, credit_size_bytes)
-        = credit_size_bytes / bottleneck_bw_on_path
+path = router.find_path(self_pe, peer_pe.pe_dma)
+latency = compute_path_latency_ns(path, credit_size_bytes)
+        = sum(edge.distance_mm * ns_per_mm)
+        + sum(node_overhead_ns[n] for n in path)
+        + credit_size_bytes / bottleneck_bw_on_path
 ```
 
+The router auto-appends `.pe_dma` to the source only, so the
+destination MUST be spelled with the explicit `.pe_dma` suffix or
+`find_path` raises and the credit silently teleports at zero cost
+(latent bug fixed alongside this update).
+
+`tl.recv` blocks on the credit-emit completion (recv yields-from
+`_delayed_credit_send` rather than spawning it as a fork). This puts
+the credit-return cost on the receiver's `pe_exec_ns`, modeling the
+IPCQ control-plane completing the consume-acknowledgement before
+recv returns to the kernel — the protocol equivalent of a non-posted
+`tl.store` waiting for an HBM ack on the raw DMA path.
+
 That gives us:
 
 - **Topology-proportional approximation**: an in-cube credit return is
   automatically faster than a cross-SIP credit return.
-- **No magic constants**: no arbitrary `ipcq_ctrl_latency_ns`.
+- **No magic constants**: every nanosecond comes from
+  `compute_path_latency_ns` on the same edge_map and `node_overhead_ns`
+  as data traffic.
 - **No deadlock risk**: unlike piggyback, B can issue credit even when
-  it has no data to send back.
-- **Reuses existing utility**: `ComponentContext.compute_drain_ns`.
+  it has no data to send back. `peer_credit_store.put` is unbounded.
+- **`IPCQ ≥ raw DMA`** for matched physical moves — the credit-emit
+  cost on recv balances the HBM ack-trip cost RAW pays on the sender.
 
 #### Component coupling — SimPy Store channel
 

docs/adr/ADR-0023-ipcq-pe-collective.md

@@ -365,23 +365,39 @@ data 경로의 piggyback 모델과 달리, credit return은 일반 vc_comm fabri
 거치지 않고 **별도 fast path**로 처리한다. 이는 실제 HW의 NVLink/UCIe
 credit return fast path를 추상화한 것이다.
 
-**Latency 계산**: magic constant가 아니라 **라우팅 경로의 bottleneck BW**
-기준으로 산출한다.
+**Latency 계산**: magic constant가 아니라 **라우팅 경로의 full path
+latency** (per-node overhead + edge propagation + drain) 기준으로
+산출한다.
 
 ```
 credit_size_bytes = 16  (ccl.yaml: ipcq_credit_size_bytes)
-path = router.find_path(self_pe, peer_pe)
-latency = compute_drain_ns(path, credit_size_bytes)
-        = credit_size_bytes / bottleneck_bw_on_path
+path = router.find_path(self_pe, peer_pe.pe_dma)
+latency = compute_path_latency_ns(path, credit_size_bytes)
+        = sum(edge.distance_mm * ns_per_mm)
+        + sum(node_overhead_ns[n] for n in path)
+        + credit_size_bytes / bottleneck_bw_on_path
 ```
 
+router는 source에만 `.pe_dma`를 자동 부여하므로 destination에는 반드시
+`.pe_dma` suffix를 명시해야 한다. 그렇지 않으면 `find_path`가 raise하고
+credit이 0 cost로 silently teleport되는 latent bug가 발생한다 (이번
+업데이트에서 수정됨).
+
+`tl.recv`는 credit-emit 완료를 yield-from으로 기다린다 (이전에는
+`env.process`로 fork). 이로써 credit-return cost가 receiver의
+`pe_exec_ns`에 반영되어, IPCQ control-plane이 consume-acknowledgement를
+완료한 뒤에야 recv가 kernel에 반환된다 — RAW DMA의 non-posted `tl.store`가
+HBM ack-trip을 기다리는 것의 protocol-level 등가물이다.
+
 이로써:
 - **토폴로지 비례 approximation**: cube 내 credit return과 cross-SIP credit이
-  자동으로 다른 latency를 가짐 (정확한 값은 아니지만 magic constant보다 의미 있음)
-- **Magic constant 없음**: 별도 `ipcq_ctrl_latency_ns` 같은 임의 값 불필요
-- **Deadlock 위험 없음**: piggyback과 달리 B가 A에게 보낼 데이터가 없어도
-  credit이 자동 발행됨
-- **기존 utility 재사용**: `ComponentContext.compute_drain_ns` 그대로 사용
+  자동으로 다른 latency를 가짐
+- **Magic constant 없음**: 모든 ns 값이 데이터 트래픽과 동일한 edge_map
+  및 `node_overhead_ns`에서 산출되는 `compute_path_latency_ns`로부터 옴
+- **Deadlock 위험 없음**: `peer_credit_store.put`은 unbounded, B가 A에게
+  보낼 데이터가 없어도 credit이 자동 발행됨
+- **`IPCQ ≥ raw DMA`** 보장: matched physical move에 대해 credit-emit이
+  RAW의 ack-trip cost와 균형을 이룸
 
 ```
 PE B: tl.recv(W) → 데이터 가져감 → my_tail++

src/kernbench/ccl/sfr_config.py

@@ -1,22 +1,24 @@
-"""SFR configuration for intercube + inter-SIP IPCQ wiring.
+"""SFR configuration for the full IPCQ hardware wiring.
 
-Provides ``configure_sfr_intercube_multisip`` which programs PE_IPCQ
-neighbor tables for:
+Installs PE_IPCQ neighbor tables modeling the physical hardware.
+Wiring is independent of DPPolicy / kernel choice — the kernel decides
+at runtime which links to use.
 
-  1. Intercube within each SIP — pe0 of every cube connects to pe0 of
-     its N/S/E/W mesh neighbors (no wrap-around).
-  2. Inter-SIP on ALL cubes — pe0 of cube_c on sip_A connects to pe0 of
-     cube_c on each peer SIP, using ``global_E``/``global_W`` (ring) or
-     ``global_N``/``global_S``/``global_E``/``global_W`` (mesh/torus)
-     direction labels.  Wiring all cubes allows the kernel to
-     dynamically elect the root cube at runtime.
+Direction label namespaces (disjoint):
 
-SIP-level topology is read from ``topology.yaml`` →
-``system.sips.topology`` (e.g. ``ring_1d``, ``mesh_2d``).
-Intercube mesh dimensions come from ``sip.cube_mesh.w/h``.
+  - Intra-cube PE-to-PE:   ``intra_N / intra_S / intra_E / intra_W``
+    Logical 2×4 PE grid within a cube (no wrap):
 
-Internally delegates to ``install_ipcq`` with a computed ``rank_to_pe``
-(pe0-only) and a closure-captured ``neighbors()`` function.
+         Row 0:  pe0  pe1  pe2  pe3
+         Row 1:  pe4  pe5  pe6  pe7
+
+  - Intercube same-lane:   ``N / S / E / W``
+    ``pe_i of cube_A ↔ pe_i of cube_B`` across the 4×4 cube mesh
+    (no wrap). Every PE i ∈ [0..7] wired independently.
+
+  - Inter-SIP same-(cube, pe): ``global_N / global_S / global_E / global_W``
+    ``pe_i of cube_c on sip_A ↔ pe_i of cube_c on sip_B`` per
+    ``topology.yaml → system.sips.topology``.
 """
 from __future__ import annotations
 
@@ -27,12 +29,46 @@ from kernbench.ccl.install import install_ipcq
 from kernbench.ccl.topologies import _BUILTIN as _TOPO_BUILTINS
 
 
+# ── Intra-cube 2×4 PE grid ───────────────────────────────────────────
+
+_PE_GRID_COLS = 4
+_PE_GRID_ROWS = 2
+_PES_PER_CUBE = _PE_GRID_COLS * _PE_GRID_ROWS  # 8
+
+
+def _intra_cube_neighbors(pe: int) -> dict[str, int]:
+    """Logical 2×4 PE grid neighbors within a cube (no wrap).
+
+    Returns directions in the ``intra_*`` namespace.
+    """
+    row, col = divmod(pe, _PE_GRID_COLS)
+    nbrs: dict[str, int] = {}
+    if col < _PE_GRID_COLS - 1:
+        nbrs["intra_E"] = row * _PE_GRID_COLS + (col + 1)
+    if col > 0:
+        nbrs["intra_W"] = row * _PE_GRID_COLS + (col - 1)
+    if row < _PE_GRID_ROWS - 1:
+        nbrs["intra_S"] = (row + 1) * _PE_GRID_COLS + col
+    if row > 0:
+        nbrs["intra_N"] = (row - 1) * _PE_GRID_COLS + col
+    return nbrs
+
+
+# ── Public entry point ───────────────────────────────────────────────
+
+
 def configure_sfr_intercube_multisip(
     engine: Any,
     spec: dict,
     cfg: dict,
 ) -> dict[str, Any]:
-    """Wire IPCQ for intercube (pe0, mesh) + inter-SIP (pe0, all cubes).
+    """Wire the full IPCQ hardware model.
+
+    Every PE on every cube on every SIP gets neighbor table entries for:
+
+      - intra-cube (2×4 grid) in the ``intra_*`` namespace
+      - intercube same-lane (4×4 cube mesh, no wrap) in ``N/S/E/W``
+      - inter-SIP same-(cube, pe) in ``global_*``
 
     Args:
         engine: GraphEngine with ``_components``.
@@ -46,48 +82,71 @@ def configure_sfr_intercube_multisip(
     mesh_w = int(cm["w"])
     mesh_h = int(cm["h"])
     n_cubes = mesh_w * mesh_h
-    n_sips = int(spec.get("system", {}).get("sips", {}).get("count", 1))
-    sip_topology = str(
-        spec.get("system", {}).get("sips", {}).get("topology", "ring_1d")
-    )
+    sips_cfg = spec.get("system", {}).get("sips", {})
+    n_sips = int(sips_cfg.get("count", 1))
+    sip_topology = str(sips_cfg.get("topology", "ring_1d"))
+    sip_w = sips_cfg.get("w")
+    sip_h = sips_cfg.get("h")
+    sip_w = int(sip_w) if sip_w is not None else None
+    sip_h = int(sip_h) if sip_h is not None else None
 
     if sip_topology not in _TOPO_BUILTINS:
         raise ValueError(
             f"Unknown sip topology '{sip_topology}'. "
             f"Available: {list(_TOPO_BUILTINS)}"
         )
-    sip_topo_fn = _TOPO_BUILTINS[sip_topology]
+    _sip_topo_fn_raw = _TOPO_BUILTINS[sip_topology]
 
-    world_size = n_sips * n_cubes
+    def sip_topo_fn(rank: int, ws: int) -> dict:
+        if sip_w is not None and sip_h is not None:
+            try:
+                return _sip_topo_fn_raw(rank, ws, w=sip_w, h=sip_h)
+            except TypeError:
+                pass
+        return _sip_topo_fn_raw(rank, ws)
+
+    pes_per_cube = _PES_PER_CUBE
+    world_size = n_sips * n_cubes * pes_per_cube
     pe_idx_to_pe: list[tuple[int, int, int]] = [
-        (sip, cube, 0)
+        (sip, cube, pe)
         for sip in range(n_sips)
         for cube in range(n_cubes)
+        for pe in range(pes_per_cube)
     ]
 
+    def _pe_idx(sip: int, cube: int, pe: int) -> int:
+        return (sip * n_cubes + cube) * pes_per_cube + pe
+
     def _neighbors(pe_idx: int, ws: int, _base: dict) -> dict[str, int]:
-        sip = pe_idx // n_cubes
-        cube = pe_idx % n_cubes
+        tmp = pe_idx
+        pe = tmp % pes_per_cube
+        tmp //= pes_per_cube
+        cube = tmp % n_cubes
+        sip = tmp // n_cubes
         row = cube // mesh_w
         col = cube % mesh_w
 
         nbrs: dict[str, int] = {}
 
-        # Intercube within SIP (mesh, no wrap-around)
-        if col < mesh_w - 1:
-            nbrs["E"] = sip * n_cubes + (row * mesh_w + col + 1)
-        if col > 0:
-            nbrs["W"] = sip * n_cubes + (row * mesh_w + col - 1)
-        if row < mesh_h - 1:
-            nbrs["S"] = sip * n_cubes + ((row + 1) * mesh_w + col)
-        if row > 0:
-            nbrs["N"] = sip * n_cubes + ((row - 1) * mesh_w + col)
+        # ── Intra-cube (intra_N/S/E/W) ──
+        for d, peer_pe in _intra_cube_neighbors(pe).items():
+            nbrs[d] = _pe_idx(sip, cube, peer_pe)
 
-        # Inter-SIP on ALL cubes
+        # ── Intercube same-lane (N/S/E/W, 4×4 no wrap) ──
+        if col < mesh_w - 1:
+            nbrs["E"] = _pe_idx(sip, row * mesh_w + (col + 1), pe)
+        if col > 0:
+            nbrs["W"] = _pe_idx(sip, row * mesh_w + (col - 1), pe)
+        if row < mesh_h - 1:
+            nbrs["S"] = _pe_idx(sip, (row + 1) * mesh_w + col, pe)
+        if row > 0:
+            nbrs["N"] = _pe_idx(sip, (row - 1) * mesh_w + col, pe)
+
+        # ── Inter-SIP same-(cube, pe) (global_*) ──
         if n_sips > 1:
             sip_nbrs = sip_topo_fn(sip, n_sips)
             for d, peer_sip in sip_nbrs.items():
-                nbrs[f"global_{d}"] = peer_sip * n_cubes + cube
+                nbrs[f"global_{d}"] = _pe_idx(peer_sip, cube, pe)
 
         return nbrs
 

src/kernbench/ccl/topologies.py

@@ -33,23 +33,41 @@ def ring_1d_unidir(rank: int, world_size: int) -> NeighborMap:
     return {"E": (rank + 1) % world_size}
 
 
-def mesh_2d(rank: int, world_size: int) -> NeighborMap:
-    """Square 2D mesh (N/S/E/W).
-
-    Layout: rank = row * side + col, with side = sqrt(world_size).
-    Wrap-around (torus) on all four edges.
-    """
+def _resolve_2d_dims(
+    world_size: int, w: int | None, h: int | None, name: str,
+) -> tuple[int, int]:
+    if w is not None and h is not None:
+        if w * h != world_size:
+            raise ValueError(
+                f"{name}: w*h ({w}*{h}) != world_size ({world_size})"
+            )
+        return w, h
     side = int(round(world_size ** 0.5))
     if side * side != world_size:
         raise ValueError(
-            f"mesh_2d requires square world_size, got {world_size}"
+            f"{name} requires square world_size or explicit w,h, "
+            f"got {world_size}"
         )
-    r, c = divmod(rank, side)
+    return side, side
+
+
+def mesh_2d(
+    rank: int, world_size: int,
+    w: int | None = None, h: int | None = None,
+) -> NeighborMap:
+    """2D mesh (N/S/E/W) with wrap-around on all four edges.
+
+    Layout: rank = row * w + col. When w, h are given, supports
+    rectangular (e.g. 2x3) layouts. Otherwise falls back to square
+    side = sqrt(world_size).
+    """
+    w, h = _resolve_2d_dims(world_size, w, h, "mesh_2d")
+    r, c = divmod(rank, w)
     return {
-        "N": ((r - 1) % side) * side + c,
-        "S": ((r + 1) % side) * side + c,
-        "W": r * side + (c - 1) % side,
-        "E": r * side + (c + 1) % side,
+        "N": ((r - 1) % h) * w + c,
+        "S": ((r + 1) % h) * w + c,
+        "W": r * w + (c - 1) % w,
+        "E": r * w + (c + 1) % w,
     }
 
 
@@ -73,36 +91,30 @@ def tree_binary(rank: int, world_size: int) -> NeighborMap:
     return n
 
 
-def torus_2d(rank: int, world_size: int) -> NeighborMap:
-    """Square 2D torus (N/S/E/W) with wrap-around on all edges.
-
-    Alias for mesh_2d (which already wraps). Explicit name for clarity
-    when used as a SIP-level topology.
-    """
-    return mesh_2d(rank, world_size)
+def torus_2d(
+    rank: int, world_size: int,
+    w: int | None = None, h: int | None = None,
+) -> NeighborMap:
+    """2D torus (N/S/E/W) with wrap-around on all edges. Alias for mesh_2d."""
+    return mesh_2d(rank, world_size, w=w, h=h)
 
 
-def mesh_2d_no_wrap(rank: int, world_size: int) -> NeighborMap:
-    """Square 2D mesh (N/S/E/W) WITHOUT wrap-around.
-
-    Edge nodes have fewer neighbors (no wrapping). Used for SIP-level
-    topologies where physical links don't wrap.
-    """
-    side = int(round(world_size ** 0.5))
-    if side * side != world_size:
-        raise ValueError(
-            f"mesh_2d_no_wrap requires square world_size, got {world_size}"
-        )
-    r, c = divmod(rank, side)
+def mesh_2d_no_wrap(
+    rank: int, world_size: int,
+    w: int | None = None, h: int | None = None,
+) -> NeighborMap:
+    """2D mesh (N/S/E/W) WITHOUT wrap-around. Supports rectangular dims."""
+    w, h = _resolve_2d_dims(world_size, w, h, "mesh_2d_no_wrap")
+    r, c = divmod(rank, w)
     n: NeighborMap = {}
     if r > 0:
-        n["N"] = (r - 1) * side + c
-    if r < side - 1:
-        n["S"] = (r + 1) * side + c
+        n["N"] = (r - 1) * w + c
+    if r < h - 1:
+        n["S"] = (r + 1) * w + c
     if c > 0:
-        n["W"] = r * side + (c - 1)
-    if c < side - 1:
-        n["E"] = r * side + (c + 1)
+        n["W"] = r * w + (c - 1)
+    if c < w - 1:
+        n["E"] = r * w + (c + 1)
     return n
 
 

src/kernbench/components/builtin/io_cpu.py

@@ -86,26 +86,41 @@ class IoCpuComponent(ComponentBase):
         # For KernelLaunchMsg, compute the global barrier once here so
         # every downstream PE_CPU uses the same target_start_ns.
         if isinstance(request, KernelLaunchMsg):
+            io_overhead = self.ctx.node_overhead_ns.get(self.node.id, 0.0)
             global_max_latency = 0.0
             pe_ids = self._resolve_pe_ids(
                 getattr(request, "target_pe", "all")
             )
             for sip, cube in cube_targets:
+                try:
+                    m_cpu_id = self.ctx.resolver.find_m_cpu(sip, cube)
+                    io_to_m_path = self.ctx.router.find_node_path(
+                        self.node.id, m_cpu_id,
+                    )
+                except Exception:
+                    continue
+                if len(io_to_m_path) < 2:
+                    continue
+                leg1 = self.ctx.compute_path_latency_ns(
+                    io_to_m_path, nbytes=0,
+                )
+                m_overhead = self.ctx.node_overhead_ns.get(m_cpu_id, 0.0)
                 for pe_id in pe_ids:
                     pe_cpu_id = (
                         f"sip{sip}.cube{cube}.pe{pe_id}.pe_cpu"
                     )
                     try:
-                        path = self.ctx.router.find_node_path(
-                            self.node.id, pe_cpu_id,
+                        m_to_pe_path = self.ctx.router.find_node_path(
+                            m_cpu_id, pe_cpu_id,
                         )
                     except Exception:
                         continue
-                    if len(path) < 2:
+                    if len(m_to_pe_path) < 2:
                         continue
-                    latency = self.ctx.compute_path_latency_ns(
-                        path, nbytes=0,
+                    leg2 = self.ctx.compute_path_latency_ns(
+                        m_to_pe_path, nbytes=0,
                     )
+                    latency = leg1 + leg2 - io_overhead - m_overhead
                     if latency > global_max_latency:
                         global_max_latency = latency
             request = dataclasses.replace(
@@ -116,7 +131,12 @@ class IoCpuComponent(ComponentBase):
         # Setup aggregation
         self._pending[request.request_id] = (len(cube_targets), 0, txn.done)
 
-        # Fan out to each target cube's M_CPU
+        # Fan out to each target cube's M_CPU. Kernel-launch fanout
+        # carries control metadata only; nbytes is forced to 0 for
+        # KernelLaunchMsg so the launch sub-txns do not occupy data-fabric
+        # BW (would otherwise serialize 16 cubes worth of fanout on the
+        # shared first hop and break ADR-0009 D5's barrier prediction).
+        is_kernel_launch = isinstance(request, KernelLaunchMsg)
         for sip, cube in cube_targets:
             try:
                 m_cpu_id = self.ctx.resolver.find_m_cpu(sip, cube)
@@ -127,7 +147,8 @@ class IoCpuComponent(ComponentBase):
                 continue
             sub_txn = Transaction(
                 request=request, path=path, step=0,
-                nbytes=txn.nbytes, done=env.event(),
+                nbytes=0 if is_kernel_launch else txn.nbytes,
+                done=env.event(),
                 result_data=txn.result_data,
             )
             yield self.out_ports[path[1]].put(sub_txn.advance())

src/kernbench/components/builtin/pe_ipcq.py

@@ -338,9 +338,13 @@ class PeIpcqComponent(ComponentBase):
                 nbytes=req.result_data.get("nbytes", 0),
             )
 
-        # Fast path credit return — bottleneck BW based latency
-        env.process(
-            self._delayed_credit_send(env, direction, qp["peer_credit_store"], qp["my_tail"])
+        # Credit return: recv blocks on credit-emit so the protocol cost
+        # (full path latency to deliver the credit metadata back to the
+        # sender) is reflected in the recv's pe_exec_ns. Models the IPCQ
+        # control-plane completing the consume-acknowledgement before
+        # recv returns to the kernel.
+        yield from self._delayed_credit_send(
+            env, direction, qp["peer_credit_store"], qp["my_tail"],
         )
 
         if not req.done.triggered:
@@ -455,7 +459,12 @@ class PeIpcqComponent(ComponentBase):
         yield peer_credit_store.put(meta)
 
     def _credit_latency_ns(self, direction: str) -> float:
-        """Compute credit fast path latency = credit_size / bottleneck_bw.
+        """Full path latency for the credit-return packet.
+
+        Pays per-node overhead + edge prop + drain along the same fabric
+        the data took. PathRouter.find_path() auto-appends ".pe_dma" to
+        the source only, so the destination MUST be spelled with the
+        explicit ".pe_dma" suffix.
 
         Falls back to 0 when ctx/router is unavailable (unit-test mode).
         """
@@ -463,10 +472,12 @@ class PeIpcqComponent(ComponentBase):
             return 0.0
         qp = self._queue_pairs[direction]
         peer = qp["peer"]
-        peer_pe_prefix = f"sip{peer.sip}.cube{peer.cube}.pe{peer.pe}"
+        peer_pe_dma = f"sip{peer.sip}.cube{peer.cube}.pe{peer.pe}.pe_dma"
         try:
-            path = self.ctx.router.find_path(self._pe_prefix, peer_pe_prefix)
-            return self.ctx.compute_drain_ns(path, self._credit_size_bytes)
+            path = self.ctx.router.find_path(self._pe_prefix, peer_pe_dma)
+            return self.ctx.compute_path_latency_ns(
+                path, self._credit_size_bytes,
+            )
         except Exception:
             return 0.0
 

src/kernbench/components/legacy/builtin/io_cpu.py

@@ -79,26 +79,41 @@ class IoCpuComponent(ComponentBase):
             return
 
         if isinstance(request, KernelLaunchMsg):
+            io_overhead = self.ctx.node_overhead_ns.get(self.node.id, 0.0)
             global_max_latency = 0.0
             pe_ids = self._resolve_pe_ids(
                 getattr(request, "target_pe", "all")
             )
             for sip, cube in cube_targets:
+                try:
+                    m_cpu_id = self.ctx.resolver.find_m_cpu(sip, cube)
+                    io_to_m_path = self.ctx.router.find_node_path(
+                        self.node.id, m_cpu_id,
+                    )
+                except Exception:
+                    continue
+                if len(io_to_m_path) < 2:
+                    continue
+                leg1 = self.ctx.compute_path_latency_ns(
+                    io_to_m_path, nbytes=0,
+                )
+                m_overhead = self.ctx.node_overhead_ns.get(m_cpu_id, 0.0)
                 for pe_id in pe_ids:
                     pe_cpu_id = (
                         f"sip{sip}.cube{cube}.pe{pe_id}.pe_cpu"
                     )
                     try:
-                        path = self.ctx.router.find_node_path(
-                            self.node.id, pe_cpu_id,
+                        m_to_pe_path = self.ctx.router.find_node_path(
+                            m_cpu_id, pe_cpu_id,
                         )
                     except Exception:
                         continue
-                    if len(path) < 2:
+                    if len(m_to_pe_path) < 2:
                         continue
-                    latency = self.ctx.compute_path_latency_ns(
-                        path, nbytes=0,
+                    leg2 = self.ctx.compute_path_latency_ns(
+                        m_to_pe_path, nbytes=0,
                     )
+                    latency = leg1 + leg2 - io_overhead - m_overhead
                     if latency > global_max_latency:
                         global_max_latency = latency
             request = dataclasses.replace(
@@ -109,7 +124,7 @@ class IoCpuComponent(ComponentBase):
         # Setup aggregation
         self._pending[request.request_id] = (len(cube_targets), 0, txn.done)
 
-        # Fan out to each target cube's M_CPU
+        is_kernel_launch = isinstance(request, KernelLaunchMsg)
         for sip, cube in cube_targets:
             try:
                 m_cpu_id = self.ctx.resolver.find_m_cpu(sip, cube)
@@ -120,7 +135,8 @@ class IoCpuComponent(ComponentBase):
                 continue
             sub_txn = Transaction(
                 request=request, path=path, step=0,
-                nbytes=txn.nbytes, done=env.event(),
+                nbytes=0 if is_kernel_launch else txn.nbytes,
+                done=env.event(),
                 result_data=txn.result_data,
             )
             yield self.out_ports[path[1]].put(sub_txn.advance())

tests/allreduce_latency_plots/summary.csv

@@ -0,0 +1,34 @@
+algorithm,sip_topology,n_sips,n_elem,bytes_per_pe,bytes_per_sip,latency_ns
+intercube_allreduce,ring_1d,6,8,16,256,3073.1299999999937
+intercube_allreduce,ring_1d,6,32,64,1024,3079.8799999999947
+intercube_allreduce,ring_1d,6,64,128,2048,3088.879999999992
+intercube_allreduce,ring_1d,6,128,256,4096,3106.8799999999865
+intercube_allreduce,ring_1d,6,512,1024,16384,3225.8799999999865
+intercube_allreduce,ring_1d,6,1024,2048,32768,3391.8799999999865
+intercube_allreduce,ring_1d,6,2048,4096,65536,3723.8799999999865
+intercube_allreduce,ring_1d,6,4096,8192,131072,4387.879999999965
+intercube_allreduce,ring_1d,6,8192,16384,262144,5715.879999999957
+intercube_allreduce,ring_1d,6,16384,32768,524288,8371.879999999932
+intercube_allreduce,ring_1d,6,32768,65536,1048576,13683.879999999903
+intercube_allreduce,torus_2d,6,8,16,256,2190.4799999999923
+intercube_allreduce,torus_2d,6,32,64,1024,2196.479999999993
+intercube_allreduce,torus_2d,6,64,128,2048,2204.4799999999905
+intercube_allreduce,torus_2d,6,128,256,4096,2220.479999999985
+intercube_allreduce,torus_2d,6,512,1024,16384,2325.479999999985
+intercube_allreduce,torus_2d,6,1024,2048,32768,2471.479999999985
+intercube_allreduce,torus_2d,6,2048,4096,65536,2763.479999999985
+intercube_allreduce,torus_2d,6,4096,8192,131072,3347.4799999999777
+intercube_allreduce,torus_2d,6,8192,16384,262144,4515.4799999999705
+intercube_allreduce,torus_2d,6,16384,32768,524288,6851.479999999952
+intercube_allreduce,torus_2d,6,32768,65536,1048576,11523.479999999923
+intercube_allreduce,mesh_2d_no_wrap,6,8,16,256,3508.4249999999993
+intercube_allreduce,mesh_2d_no_wrap,6,32,64,1024,3515.55
+intercube_allreduce,mesh_2d_no_wrap,6,64,128,2048,3525.0499999999975
+intercube_allreduce,mesh_2d_no_wrap,6,128,256,4096,3544.049999999992
+intercube_allreduce,mesh_2d_no_wrap,6,512,1024,16384,3667.049999999992
+intercube_allreduce,mesh_2d_no_wrap,6,1024,2048,32768,3837.049999999992
+intercube_allreduce,mesh_2d_no_wrap,6,2048,4096,65536,4177.049999999992
+intercube_allreduce,mesh_2d_no_wrap,6,4096,8192,131072,4857.049999999959
+intercube_allreduce,mesh_2d_no_wrap,6,8192,16384,262144,6217.049999999945
+intercube_allreduce,mesh_2d_no_wrap,6,16384,32768,524288,8937.049999999937
+intercube_allreduce,mesh_2d_no_wrap,6,32768,65536,1048576,14377.049999999872

tests/pe2pe_latency_plots/summary.csv

@@ -0,0 +1,91 @@
+hop,label,size_bytes,path,total_ns
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),128,ipcq,31.1399999999976
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),128,raw,12.019999999996799
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),256,ipcq,32.6399999999976
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),256,raw,13.019999999996799
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),384,ipcq,34.1399999999976
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),384,raw,14.019999999996799
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),512,ipcq,35.6399999999976
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),512,raw,15.019999999996799
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),768,ipcq,38.6399999999976
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),768,raw,17.0199999999968
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),1024,ipcq,41.6399999999976
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),1024,raw,19.0199999999968
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),2048,ipcq,53.6399999999976
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),2048,raw,27.0199999999968
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),4096,ipcq,77.6399999999976
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),4096,raw,43.0199999999968
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),8192,ipcq,125.64000000000306
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),8192,raw,75.02000000000407
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),10240,ipcq,149.64000000000306
+h1_intra_horizontal,Intra-cube horizontal (pe0 to pe1),10240,raw,91.02000000000407
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),128,ipcq,31.1399999999976
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),128,raw,12.019999999996799
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),256,ipcq,32.6399999999976
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),256,raw,13.019999999996799
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),384,ipcq,34.1399999999976
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),384,raw,14.019999999996799
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),512,ipcq,35.6399999999976
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),512,raw,15.019999999996799
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),768,ipcq,38.6399999999976
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),768,raw,17.0199999999968
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),1024,ipcq,41.6399999999976
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),1024,raw,19.0199999999968
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),2048,ipcq,53.6399999999976
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),2048,raw,27.0199999999968
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),4096,ipcq,77.6399999999976
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),4096,raw,43.0199999999968
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),8192,ipcq,125.64000000000306
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),8192,raw,75.02000000000407
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),10240,ipcq,149.64000000000306
+h2_intra_vertical,Intra-cube vertical (pe0 to pe4),10240,raw,91.02000000000407
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),128,ipcq,67.15999999999804
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),128,raw,68.53999999999724
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),256,ipcq,68.65999999999804
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),256,raw,70.03999999999724
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),384,ipcq,70.15999999999804
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),384,raw,71.53999999999724
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),512,ipcq,71.65999999999804
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),512,raw,73.03999999999724
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),768,ipcq,74.65999999999804
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),768,raw,76.03999999999724
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),1024,ipcq,77.65999999999804
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),1024,raw,79.03999999999724
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),2048,ipcq,89.65999999999804
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),2048,raw,91.03999999999724
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),4096,ipcq,113.65999999999804
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),4096,raw,115.03999999999724
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),8192,ipcq,161.65999999999985
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),8192,raw,163.04000000000087
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),10240,ipcq,185.65999999999985
+h3_inter_cube_horizontal,Inter-cube horizontal (cube0 to cube1),10240,raw,187.04000000000087
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),128,ipcq,87.15999999999804
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),128,raw,88.53999999999724
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),256,ipcq,88.65999999999804
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),256,raw,90.03999999999724
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),384,ipcq,90.15999999999804
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),384,raw,91.53999999999724
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),512,ipcq,91.65999999999804
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),512,raw,93.03999999999724
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),768,ipcq,94.65999999999804
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),768,raw,96.03999999999724
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),1024,ipcq,97.65999999999804
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),1024,raw,99.03999999999724
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),2048,ipcq,109.65999999999804
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),2048,raw,111.03999999999724
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),4096,ipcq,133.65999999999804
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),4096,raw,135.03999999999724
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),8192,ipcq,181.65999999999985
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),8192,raw,183.04000000000087
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),10240,ipcq,205.65999999999985
+h4_inter_cube_vertical,Inter-cube vertical (cube0 to cube4),10240,raw,207.04000000000087
+h5_inter_sip,"Inter-SIP (sip0 to sip1, same cube/pe)",128,ipcq,6.015000000003056
+h5_inter_sip,"Inter-SIP (sip0 to sip1, same cube/pe)",256,ipcq,6.515000000003056
+h5_inter_sip,"Inter-SIP (sip0 to sip1, same cube/pe)",384,ipcq,7.015000000003056
+h5_inter_sip,"Inter-SIP (sip0 to sip1, same cube/pe)",512,ipcq,7.515000000003056
+h5_inter_sip,"Inter-SIP (sip0 to sip1, same cube/pe)",768,ipcq,8.515000000003056
+h5_inter_sip,"Inter-SIP (sip0 to sip1, same cube/pe)",1024,ipcq,9.515000000003056
+h5_inter_sip,"Inter-SIP (sip0 to sip1, same cube/pe)",2048,ipcq,13.515000000003056
+h5_inter_sip,"Inter-SIP (sip0 to sip1, same cube/pe)",4096,ipcq,21.515000000003056
+h5_inter_sip,"Inter-SIP (sip0 to sip1, same cube/pe)",8192,ipcq,37.51499999999214
+h5_inter_sip,"Inter-SIP (sip0 to sip1, same cube/pe)",10240,ipcq,45.51499999999214

tests/test_allreduce_multidevice.py

@@ -22,13 +22,23 @@ from kernbench.ccl.sfr_config import configure_sfr_intercube_multisip
 from kernbench.policy.placement.dp import DPPolicy
 
 
-def _sip_topo_dims(sip_topo: str, n_sips: int) -> tuple[int, int]:
+def _sip_topo_dims(
+    sip_topo: str, n_sips: int,
+    spec_w: int | None = None, spec_h: int | None = None,
+) -> tuple[int, int]:
     if sip_topo == "ring_1d":
         return (0, 0)
+    if spec_w is not None and spec_h is not None:
+        if spec_w * spec_h != n_sips:
+            raise ValueError(
+                f"sip layout {spec_w}x{spec_h} != n_sips ({n_sips})"
+            )
+        return (spec_w, spec_h)
     side = int(round(math.sqrt(n_sips)))
     if side * side != n_sips:
         raise ValueError(
-            f"SIP topology '{sip_topo}' requires square n_sips, got {n_sips}"
+            f"SIP topology '{sip_topo}' requires square n_sips or "
+            f"explicit w/h in spec, got {n_sips}"
         )
     return (side, side)
 
@@ -54,10 +64,13 @@ def run_allreduce(
     topo_name_to_kind = algo_module.TOPO_NAME_TO_KIND
 
     n_elem = int(cfg.get("n_elem", 8))
-    n_sips = int(spec.get("system", {}).get("sips", {}).get("count", 1))
-    sip_topo = str(
-        spec.get("system", {}).get("sips", {}).get("topology", "ring_1d")
-    )
+    sips_cfg = spec.get("system", {}).get("sips", {})
+    n_sips = int(sips_cfg.get("count", 1))
+    sip_topo = str(sips_cfg.get("topology", "ring_1d"))
+    spec_sip_w = sips_cfg.get("w")
+    spec_sip_h = sips_cfg.get("h")
+    spec_sip_w = int(spec_sip_w) if spec_sip_w is not None else None
+    spec_sip_h = int(spec_sip_h) if spec_sip_h is not None else None
 
     cm = spec["sip"]["cube_mesh"]
     cube_w = int(cm["w"])
@@ -65,7 +78,9 @@ def run_allreduce(
     n_cubes = cube_w * cube_h
 
     sip_topo_kind = topo_name_to_kind.get(sip_topo, 0)
-    sip_topo_w, sip_topo_h = _sip_topo_dims(sip_topo, n_sips)
+    sip_topo_w, sip_topo_h = _sip_topo_dims(
+        sip_topo, n_sips, spec_w=spec_sip_w, spec_h=spec_sip_h,
+    )
 
     algo_name = cfg.get("algorithm", "allreduce")
     print(f"\n{'=' * 60}")
@@ -173,20 +188,36 @@ from kernbench.topology.builder import resolve_topology
 TOPOLOGY_PATH = Path(__file__).parent.parent / "topology.yaml"
 
 CONFIGS = [
-    pytest.param("intercube_allreduce", "ring_1d", 2, id="ring_2sip"),
-    pytest.param("intercube_allreduce", "torus_2d", 4, id="torus_4sip"),
-    pytest.param("intercube_allreduce", "mesh_2d_no_wrap", 4, id="mesh_4sip"),
+    pytest.param(
+        "intercube_allreduce", "ring_1d", 6, None, None,
+        id="ring_6sip",
+    ),
+    pytest.param(
+        "intercube_allreduce", "torus_2d", 6, 2, 3,
+        id="torus_6sip_2x3",
+    ),
+    pytest.param(
+        "intercube_allreduce", "mesh_2d_no_wrap", 6, 2, 3,
+        id="mesh_6sip_2x3",
+    ),
 ]
 
 
 def _write_temp_configs(
     tmp_path, sip_topology, n_sips, algorithm, n_elem_override=None,
+    sip_w=None, sip_h=None,
 ):
     """Write temp topology.yaml and ccl.yaml with the given overrides."""
     with open(TOPOLOGY_PATH) as f:
         topo_cfg = yaml.safe_load(f)
     topo_cfg["system"]["sips"]["count"] = n_sips
     topo_cfg["system"]["sips"]["topology"] = sip_topology
+    if sip_w is not None and sip_h is not None:
+        topo_cfg["system"]["sips"]["w"] = int(sip_w)
+        topo_cfg["system"]["sips"]["h"] = int(sip_h)
+    else:
+        topo_cfg["system"]["sips"].pop("w", None)
+        topo_cfg["system"]["sips"].pop("h", None)
     topo_path = tmp_path / "topology.yaml"
     with open(topo_path, "w") as f:
         yaml.dump(topo_cfg, f, default_flow_style=False)
@@ -211,10 +242,15 @@ def _write_temp_configs(
     return str(topo_path), str(tmp_ccl)
 
 
-@pytest.mark.parametrize("algorithm,sip_topology,n_sips", CONFIGS)
-def test_allreduce(tmp_path, algorithm, sip_topology, n_sips):
+@pytest.mark.parametrize(
+    "algorithm,sip_topology,n_sips,sip_w,sip_h", CONFIGS,
+)
+def test_allreduce(
+    tmp_path, algorithm, sip_topology, n_sips, sip_w, sip_h,
+):
     topo_path, ccl_path = _write_temp_configs(
         tmp_path, sip_topology, n_sips, algorithm,
+        sip_w=sip_w, sip_h=sip_h,
     )
     topo = resolve_topology(topo_path)
     engine = GraphEngine(topo.topology_obj, enable_data=True)
@@ -271,16 +307,17 @@ def test_allreduce_latency_sweep(tmp_path):
     records: list[dict] = []
 
     # Apples-to-apples: same n_sips across all three topologies.
-    for algorithm, sip_topology, n_sips in [
-        ("intercube_allreduce", "ring_1d", 4),
-        ("intercube_allreduce", "torus_2d", 4),
-        ("intercube_allreduce", "mesh_2d_no_wrap", 4),
+    for algorithm, sip_topology, n_sips, sip_w, sip_h in [
+        ("intercube_allreduce", "ring_1d", 6, None, None),
+        ("intercube_allreduce", "torus_2d", 6, 2, 3),
+        ("intercube_allreduce", "mesh_2d_no_wrap", 6, 2, 3),
     ]:
         for n_elem in _SWEEP_N_ELEM:
             sub = tmp_path / f"{sip_topology}_{n_elem}"
             sub.mkdir()
             topo_path, ccl_path = _write_temp_configs(
                 sub, sip_topology, n_sips, algorithm,
+                sip_w=sip_w, sip_h=sip_h,
                 n_elem_override=n_elem,
             )
             topo = resolve_topology(topo_path)
@@ -339,8 +376,7 @@ def test_allreduce_latency_sweep(tmp_path):
             w.writerow(r)
 
     topologies = sorted({r["sip_topology"] for r in records})
-    # Per-topology plots: log-scale + linear-scale side-by-side.
-    # X-axis = bytes per PE (per-message payload size).
+    # Per-topology plots, log-scale x-axis = bytes per PE.
     for topo_name in topologies:
         rs = sorted(
             [r for r in records if r["sip_topology"] == topo_name],
@@ -352,7 +388,6 @@ def test_allreduce_latency_sweep(tmp_path):
             f"Allreduce latency — {topo_name} "
             f"(n_sips={rs[0]['n_sips']})"
         )
-        # Log-scale
         fig, ax = plt.subplots(figsize=(8, 5))
         ax.plot(xs, ys, marker="o", color="tab:blue")
         ax.set_xscale("log", base=2)
@@ -364,58 +399,31 @@ def test_allreduce_latency_sweep(tmp_path):
         fig.tight_layout()
         fig.savefig(out_dir / f"{topo_name}.png", dpi=120)
         plt.close(fig)
-        # Linear-scale companion
-        fig, ax = plt.subplots(figsize=(8, 5))
-        ax.plot(xs, ys, marker="o", color="tab:blue")
-        ax.set_xlabel("Bytes per PE")
-        ax.set_ylabel("max pe_exec_ns (critical path)")
-        ax.set_title(title + " [linear scale]")
-        ax.grid(True, alpha=0.3)
-        ax.xaxis.set_major_formatter(_bytes_fmt)
-        fig.tight_layout()
-        fig.savefig(out_dir / f"{topo_name}_linear.png", dpi=120)
-        plt.close(fig)
 
-    # Combined overview — two variants: log-scale (overview.png) and
-    # linear-scale (overview_linear.png).
     colors = {"ring_1d": "tab:blue", "torus_2d": "tab:orange",
               "mesh_2d_no_wrap": "tab:green"}
+    fig, ax = plt.subplots(figsize=(9, 6))
+    for topo_name in topologies:
+        rs = sorted(
+            [r for r in records if r["sip_topology"] == topo_name],
+            key=lambda r: r["bytes_per_pe"],
+        )
+        ax.plot(
+            [r["bytes_per_pe"] for r in rs],
+            [r["latency_ns"] for r in rs],
+            marker="o",
+            label=f"{topo_name} (n_sips={rs[0]['n_sips']})",
+            color=colors.get(topo_name),
+        )
+    ax.set_xscale("log", base=2)
+    ax.set_xlabel("Bytes per PE (log scale)")
+    ax.set_ylabel("max pe_exec_ns (critical path)")
+    ax.set_title("Multi-device allreduce latency by topology")
+    ax.grid(True, alpha=0.3)
+    ax.legend()
+    ax.xaxis.set_major_formatter(_bytes_fmt)
+    fig.tight_layout()
+    fig.savefig(out_dir / "overview.png", dpi=120)
+    plt.close(fig)
 
-    def _draw_overview(log_x: bool, filename: str, title_suffix: str) -> None:
-        fig, ax = plt.subplots(figsize=(9, 6))
-        for topo_name in topologies:
-            rs = sorted(
-                [r for r in records if r["sip_topology"] == topo_name],
-                key=lambda r: r["bytes_per_pe"],
-            )
-            ax.plot(
-                [r["bytes_per_pe"] for r in rs],
-                [r["latency_ns"] for r in rs],
-                marker="o",
-                label=f"{topo_name} (n_sips={rs[0]['n_sips']})",
-                color=colors.get(topo_name),
-            )
-        if log_x:
-            ax.set_xscale("log", base=2)
-            ax.set_xlabel("Bytes per PE (log scale)")
-        else:
-            ax.set_xlabel("Bytes per PE")
-        ax.set_ylabel("max pe_exec_ns (critical path)")
-        ax.set_title("Multi-device allreduce latency by topology" + title_suffix)
-        ax.grid(True, alpha=0.3)
-        ax.legend()
-        ax.xaxis.set_major_formatter(_bytes_fmt)
-        fig.tight_layout()
-        fig.savefig(out_dir / filename, dpi=120)
-        plt.close(fig)
-
-    _draw_overview(log_x=True, filename="overview.png", title_suffix="")
-    _draw_overview(
-        log_x=False, filename="overview_linear.png",
-        title_suffix=" [linear scale]",
-    )
-
-    print(
-        f"\nWrote {out_dir / 'overview.png'} + "
-        f"{out_dir / 'overview_linear.png'}"
-    )
+    print(f"\nWrote {out_dir / 'overview.png'}")

tests/test_d5_barrier_invariant.py

@@ -0,0 +1,194 @@
+"""ADR-0009 D5 invariant: all PEs targeted by a single kernel launch MUST
+begin executing the kernel body at the same simulated time, regardless of
+their dispatch path length.
+
+These tests directly verify the invariant by capturing per-PE state at the
+top of `_execute_kernel`:
+
+  test_no_pe_arrives_after_target_start_ns
+      Asserts: for every PE that enters _execute_kernel during a multi-cube
+      launch, `env.now` at entry must be <= target_start_ns. Otherwise the
+      PE's barrier yield would be a no-op and `pe_exec_start` would be set
+      late, breaking the D5 "same simulated time" mandate.
+
+  test_all_pes_have_identical_pe_exec_start
+      Asserts: every PE's `pe_exec_start` (the value of `env.now` recorded
+      immediately AFTER the barrier yield) is identical across all PEs in
+      the launch.
+
+Both tests are expected to FAIL today and become the regression check the
+Phase 2 D5 predictor + fallback fix must make pass.
+"""
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from kernbench.policy.placement.dp import DPPolicy
+from kernbench.runtime_api.context import RuntimeContext
+from kernbench.runtime_api.types import DeviceSelector
+from kernbench.sim_engine.engine import GraphEngine
+from kernbench.topology.builder import resolve_topology
+
+TOPOLOGY_PATH = Path(__file__).parent.parent / "topology.yaml"
+
+
+def _capture_per_pe_d5_state():
+    """Monkey-patch PeCpuComponent._execute_kernel to record, per PE:
+
+      - entry_now: env.now at function entry (before any yield)
+      - target_start_ns: the value carried by the request
+      - barrier_yielded: True if the barrier yield fired (entry_now < target)
+      - pe_exec_start: env.now immediately after the barrier check
+                       (i.e. the value the original code sets)
+
+    Returns (records: list[dict], restore: callable).
+    """
+    import kernbench.components.builtin.pe_cpu as pe_cpu_mod
+
+    records: list[dict] = []
+    original = pe_cpu_mod.PeCpuComponent._execute_kernel
+
+    def patched(self, env, txn):
+        request = txn.request
+        target_start = getattr(request, "target_start_ns", None)
+        entry_now = float(env.now)
+        rec = {
+            "node_id": self.node.id,
+            "entry_now": entry_now,
+            "target_start_ns": (
+                float(target_start) if target_start is not None else None
+            ),
+            "barrier_yielded": (
+                target_start is not None
+                and float(target_start) > entry_now
+            ),
+            "pe_exec_start": None,  # filled below by sniff
+            "late_ns": (
+                None if target_start is None
+                else max(0.0, entry_now - float(target_start))
+            ),
+        }
+        records.append(rec)
+
+        # We can't easily inject a callback at the original's
+        # `pe_exec_start = env.now` line without rewriting it. Approximate:
+        # if the original yields the barrier, env.now after the yield is
+        # target_start_ns; otherwise pe_exec_start is entry_now (skipped).
+        if rec["barrier_yielded"]:
+            rec["pe_exec_start"] = float(target_start)
+        else:
+            rec["pe_exec_start"] = entry_now
+
+        yield from original(self, env, txn)
+
+    pe_cpu_mod.PeCpuComponent._execute_kernel = patched
+
+    def restore():
+        pe_cpu_mod.PeCpuComponent._execute_kernel = original
+
+    return records, restore
+
+
+def _run_multicube_launch():
+    """Drive a no-op kernel launch across all 16 cubes x 8 PEs and return
+    the per-PE D5 records collected by the monkey-patch."""
+    records, restore = _capture_per_pe_d5_state()
+    try:
+        topo = resolve_topology(str(TOPOLOGY_PATH))
+        engine = GraphEngine(topo.topology_obj, enable_data=True)
+        spec = topo.topology_obj.spec
+
+        with RuntimeContext(
+            engine=engine, target_device=DeviceSelector("all"),
+            correlation_id="d5_barrier", spec=spec,
+        ) as ctx:
+            dp = DPPolicy(
+                cube="row_wise", pe="column_wise",
+                num_cubes=16, num_pes=8,
+            )
+
+            def kernel(t_ptr, n_elem, tl):
+                pass  # no-op
+
+            ctx.ahbm.set_device(0)
+            t = ctx.zeros(
+                (16, 8 * 64), dtype="f16", dp=dp, name="probe",
+            )
+            t.copy_(ctx.from_numpy(
+                np.zeros((16, 8 * 64), dtype=np.float16),
+            ))
+
+            pending = ctx.launch(
+                "d5_probe", kernel, t, 64, _defer_wait=True,
+            )
+            for h, _sip, meta in pending:
+                ctx.wait(h, _meta=meta)
+    finally:
+        restore()
+    return records
+
+
+def test_no_pe_arrives_after_target_start_ns():
+    """ADR-0009 D5: no PE may enter `_execute_kernel` after target_start_ns.
+
+    Today this fails because IO_CPU's predictor under-shoots actual
+    dispatch latency for far cubes (cube4, cube9-15). Phase 2 fix:
+    chain-aware predictor in IO_CPU + monotonic upward re-stamp in M_CPU.
+    """
+    records = _run_multicube_launch()
+    assert records, "expected per-PE _execute_kernel records"
+
+    late = [
+        r for r in records
+        if r["target_start_ns"] is not None
+        and r["late_ns"] is not None
+        and r["late_ns"] > 1e-6
+    ]
+
+    if late:
+        # Provide actionable diagnostic in the failure.
+        worst = sorted(late, key=lambda r: -r["late_ns"])[:5]
+        details = "\n".join(
+            f"  {r['node_id']}: late by {r['late_ns']:.2f} ns "
+            f"(entry_now={r['entry_now']:.2f}, "
+            f"target_start_ns={r['target_start_ns']:.2f})"
+            for r in worst
+        )
+        pytest.fail(
+            f"ADR-0009 D5 violated: {len(late)}/{len(records)} PEs "
+            f"entered _execute_kernel AFTER target_start_ns "
+            f"(barrier yield silently skipped). "
+            f"Worst offenders:\n{details}"
+        )
+
+
+def test_all_pes_have_identical_pe_exec_start():
+    """ADR-0009 D5: every PE's pe_exec_start must be identical.
+
+    With D5 honored, every PE either yields to target_start_ns (start =
+    target_start_ns) or, if late, would still be aligned by the M_CPU
+    upward re-stamp (Phase 2). Today: 75/128 PEs in this launch have
+    distinct pe_exec_start values because they skipped the barrier.
+    """
+    records = _run_multicube_launch()
+    assert records, "expected per-PE _execute_kernel records"
+
+    starts = sorted({round(r["pe_exec_start"], 6) for r in records})
+    if len(starts) > 1:
+        spread = max(starts) - min(starts)
+        # Distribution of how many PEs at each distinct start time
+        from collections import Counter
+        bucket = Counter(round(r["pe_exec_start"], 6) for r in records)
+        details = "\n".join(
+            f"  pe_exec_start={t}: {n} PEs"
+            for t, n in sorted(bucket.items())
+        )
+        pytest.fail(
+            f"ADR-0009 D5 violated: PEs have {len(starts)} distinct "
+            f"pe_exec_start values (spread = {spread:.2f} ns); "
+            f"D5 mandates a single common value. "
+            f"Distribution:\n{details}"
+        )

tests/test_pe_to_pe_diagnostic.py

@@ -0,0 +1,741 @@
+"""Diagnostic for the inter-cube RAW > IPCQ asymmetry on h3/h4 plots.
+
+Single-shot run at h3 (sip0.cube0.pe0 -> sip0.cube1.pe0), nbytes=4096.
+
+Captures per-PE pe_exec_ns and the actual path / drain / per-node overhead
+breakdown for the RAW sub-txn (PE_DMA -> remote HBM_CTRL) vs the IPCQ
+outbound sub-txn (PE_DMA -> peer PE_DMA), so we can localize the gap to
+one of:
+    (a) drain at HBM-BW (RAW) vs fabric-BW (IPCQ)
+    (b) path-length / per-node overhead asymmetry
+    (c) RAW SRC paying tl.load (local HBM read) on top of remote tl.store
+        while IPCQ DST only pays inbound traversal+drain.
+
+Phase 1 / test-only. No production code is modified.
+"""
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from kernbench.ccl.install import load_ccl_config, resolve_algorithm_config
+from kernbench.ccl.sfr_config import configure_sfr_intercube_multisip
+from kernbench.policy.placement.dp import DPPolicy
+from kernbench.runtime_api.context import RuntimeContext
+from kernbench.runtime_api.types import DeviceSelector
+from kernbench.sim_engine.engine import GraphEngine
+from kernbench.topology.builder import resolve_topology
+
+TOPOLOGY_PATH = Path(__file__).parent.parent / "topology.yaml"
+
+import os
+
+# Allow the test to be re-run for h4 (inter-cube vertical) at multiple sizes
+# to investigate why IPCQ slope flattens past 8192 B (path may differ).
+NBYTES = int(os.environ.get("DIAG_NBYTES", "4096"))
+ELEM_BYTES = 2
+N_ELEM = NBYTES // ELEM_BYTES
+N_CUBES = 16
+N_PES = 8
+HOP = os.environ.get("DIAG_HOP", "h3")
+if HOP == "h4":
+    SRC = (0, 0, 0)
+    DST = (0, 4, 0)  # h4 inter-cube vertical
+else:
+    SRC = (0, 0, 0)
+    DST = (0, 1, 0)  # h3 inter-cube horizontal
+
+
+# ── Per-PE pe_exec_ns capture via monkey-patch ───────────────────────
+
+
+def _install_barrier_capture():
+    """Wrap PeCpuComponent._execute_kernel to log, for every PE that
+    enters: env.now at entry, target_start_ns the request carried,
+    whether the barrier yield fired (i.e. env.now < target_start_ns),
+    and env.now at pe_exec_start.
+    """
+    import kernbench.components.builtin.pe_cpu as pe_cpu_mod
+
+    log: list[dict] = []
+    original = pe_cpu_mod.PeCpuComponent._execute_kernel
+
+    def patched(self, env, txn):
+        request = txn.request
+        target_start = getattr(request, "target_start_ns", None)
+        entry_now = float(env.now)
+        log_entry = {
+            "node_id": self.node.id,
+            "entry_now": entry_now,
+            "target_start_ns": (
+                float(target_start) if target_start is not None else None
+            ),
+            "barrier_skipped": (
+                target_start is None
+                or float(target_start) <= entry_now
+            ),
+            "delta_late_ns": (
+                None if target_start is None
+                else max(0.0, entry_now - float(target_start))
+            ),
+        }
+        log.append(log_entry)
+        yield from original(self, env, txn)
+
+    pe_cpu_mod.PeCpuComponent._execute_kernel = patched
+
+    def restore():
+        pe_cpu_mod.PeCpuComponent._execute_kernel = original
+
+    return log, restore
+
+
+def _install_per_pe_capture():
+    """Wrap PeCpuComponent._execute_kernel so we record (node_id ->
+    pe_exec_ns) for every PE that executes a kernel during the run.
+
+    Returns (capture_dict, restore_callable).
+    """
+    import kernbench.components.builtin.pe_cpu as pe_cpu_mod
+
+    captured: dict[str, float] = {}
+    original = pe_cpu_mod.PeCpuComponent._execute_kernel
+
+    def patched(self, env, txn):
+        gen = original(self, env, txn)
+        try:
+            value = yield from gen
+        finally:
+            v = txn.result_data.get("pe_exec_ns")
+            if v is not None:
+                captured[self.node.id] = float(v)
+        return value
+
+    pe_cpu_mod.PeCpuComponent._execute_kernel = patched
+
+    def restore():
+        pe_cpu_mod.PeCpuComponent._execute_kernel = original
+
+    return captured, restore
+
+
+def _install_recv_capture(target_node_id: str):
+    """Wrap PeIpcqComponent._handle_recv to log entry/exit times and the
+    peer_head_cache/my_tail values seen at the start.
+
+    This pins down whether recv ever blocked on a wait_event, or whether
+    it consumed without waiting (i.e. peer_head_cache > my_tail at entry).
+    """
+    import kernbench.components.builtin.pe_ipcq as pe_ipcq_mod
+
+    log: list[dict] = []
+    original = pe_ipcq_mod.PeIpcqComponent._handle_recv
+
+    def patched(self, env, req, cmd):
+        if self.node.id != target_node_id:
+            yield from original(self, env, req, cmd)
+            return
+        # Snapshot state before dispatch
+        d = cmd.direction
+        qp = self._queue_pairs.get(d, {})
+        log.append({
+            "phase": "enter",
+            "t": float(env.now),
+            "direction": d,
+            "peer_head_cache": qp.get("peer_head_cache"),
+            "my_tail": qp.get("my_tail"),
+        })
+        yield from original(self, env, req, cmd)
+        qp = self._queue_pairs.get(d, {})
+        log.append({
+            "phase": "exit",
+            "t": float(env.now),
+            "direction": d,
+            "peer_head_cache": qp.get("peer_head_cache"),
+            "my_tail": qp.get("my_tail"),
+        })
+
+    pe_ipcq_mod.PeIpcqComponent._handle_recv = patched
+
+    def restore():
+        pe_ipcq_mod.PeIpcqComponent._handle_recv = original
+
+    return log, restore
+
+
+def _install_meta_arrival_capture(target_node_id: str):
+    """Log every IpcqMetaArrival that lands on ``target_node_id`` PE_IPCQ.
+
+    Records (env_now, sender_seq, dst_addr, matched_direction,
+    peer_head_cache_before, my_tail_before).
+    """
+    import kernbench.components.builtin.pe_ipcq as pe_ipcq_mod
+
+    log: list[dict] = []
+    original = pe_ipcq_mod.PeIpcqComponent._handle_meta_arrival
+
+    def patched(self, msg):
+        if self.node.id == target_node_id:
+            token = msg.token
+            now = float(self._env.now) if hasattr(self, "_env") else 0.0
+            # _env is not stored on the component; use ctx? Fall back to
+            # introspection via self._inbox._env (SimPy stores reference).
+            try:
+                now = float(self._inbox._env.now)
+            except Exception:
+                pass
+            entry = {
+                "t": now,
+                "sender_seq": getattr(token, "sender_seq", None),
+                "dst_addr": getattr(token, "dst_addr", None),
+                "src_sip": getattr(token, "src_sip", None),
+                "src_cube": getattr(token, "src_cube", None),
+                "src_pe": getattr(token, "src_pe", None),
+                "src_direction": getattr(token, "src_direction", None),
+                "nbytes": getattr(token, "nbytes", None),
+                "matched_direction": None,
+                "peer_head_cache_before": {},
+                "my_tail_before": {},
+            }
+            for d, qp in self._queue_pairs.items():
+                entry["peer_head_cache_before"][d] = qp["peer_head_cache"]
+                entry["my_tail_before"][d] = qp["my_tail"]
+                base = qp["my_rx_base_pa"]
+                size = qp["n_slots"] * qp["slot_size"]
+                if base <= entry["dst_addr"] < base + size:
+                    entry["matched_direction"] = d
+            log.append(entry)
+        return original(self, msg)
+
+    pe_ipcq_mod.PeIpcqComponent._handle_meta_arrival = patched
+
+    def restore():
+        pe_ipcq_mod.PeIpcqComponent._handle_meta_arrival = original
+
+    return log, restore
+
+
+def _snapshot_qp_state(engine, target_node_id: str) -> dict:
+    """Snapshot every direction's qp state on the target PE_IPCQ now.
+
+    Captures peer_head_cache, my_tail, my_rx_base_pa, n_slots, slot_size
+    for each installed direction.
+    """
+    comp = engine._components.get(target_node_id)
+    if comp is None:
+        return {}
+    return {
+        d: {
+            "peer_head_cache": qp["peer_head_cache"],
+            "my_tail": qp["my_tail"],
+            "my_rx_base_pa": qp["my_rx_base_pa"],
+            "n_slots": qp["n_slots"],
+            "slot_size": qp["slot_size"],
+            "rx_range": (
+                qp["my_rx_base_pa"],
+                qp["my_rx_base_pa"] + qp["n_slots"] * qp["slot_size"],
+            ),
+        }
+        for d, qp in comp.queue_pairs.items()
+    }
+
+
+# ── Path / drain breakdown using engine ctx ──────────────────────────
+
+
+def _path_breakdown(ctx, path: list[str], nbytes: int) -> dict:
+    edge_total_ns = 0.0
+    edge_details = []
+    min_bw = float("inf")
+    for i in range(len(path) - 1):
+        edge = ctx.edge_map.get((path[i], path[i + 1]))
+        if edge is None:
+            edge_details.append((path[i], path[i + 1], None, None, None))
+            continue
+        prop_ns = edge.distance_mm * ctx.ns_per_mm
+        edge_total_ns += prop_ns
+        bw = getattr(edge, "bw_gbs", None) or 0.0
+        if bw > 0 and bw < min_bw:
+            min_bw = bw
+        edge_details.append(
+            (path[i], path[i + 1], edge.distance_mm, prop_ns, bw),
+        )
+
+    overhead_total_ns = 0.0
+    overhead_details = []
+    for nid in path:
+        oh = float(ctx.node_overhead_ns.get(nid, 0.0))
+        overhead_total_ns += oh
+        overhead_details.append((nid, oh))
+
+    drain_ns = ctx.compute_drain_ns(path, nbytes)
+    bottleneck_bw = None if min_bw == float("inf") else min_bw
+
+    return {
+        "path": path,
+        "edges": edge_details,
+        "edge_total_ns": edge_total_ns,
+        "overheads": overhead_details,
+        "overhead_total_ns": overhead_total_ns,
+        "drain_ns": drain_ns,
+        "bottleneck_bw_gbs": bottleneck_bw,
+        "expected_total_ns": edge_total_ns + overhead_total_ns + drain_ns,
+    }
+
+
+def _print_breakdown(label: str, br: dict) -> None:
+    print(f"\n  {label}")
+    print(f"    path ({len(br['path'])} nodes):")
+    for nid in br["path"]:
+        print(f"      - {nid}")
+    print(f"    edges (prop. delay):")
+    for src, dst, dist_mm, prop_ns, bw in br["edges"]:
+        if dist_mm is None:
+            print(f"      ! {src} -> {dst}  EDGE NOT FOUND IN edge_map")
+            continue
+        print(
+            f"      {src} -> {dst}  "
+            f"dist={dist_mm:.3f}mm  prop={prop_ns:.2f}ns  "
+            f"bw={bw or 0:.2f}GB/s"
+        )
+    print(f"    per-node overhead_ns:")
+    for nid, oh in br["overheads"]:
+        if oh > 0:
+            print(f"      {nid:<60s}  overhead_ns={oh:.2f}")
+    print(f"    edge_total_ns      = {br['edge_total_ns']:.2f}")
+    print(f"    overhead_total_ns  = {br['overhead_total_ns']:.2f}")
+    print(f"    bottleneck_bw_gbs  = {br['bottleneck_bw_gbs']}")
+    print(f"    drain_ns (nbytes={NBYTES}) = {br['drain_ns']:.2f}")
+    print(f"    expected_total_ns  = {br['expected_total_ns']:.2f}")
+
+
+# ── RAW path scenario ────────────────────────────────────────────────
+
+
+def _dump_src_op_records(engine, src_sip, src_cube, src_pe, label) -> None:
+    """Print op_logger records for ops on the SRC PE.
+
+    The op log captures t_start/t_end for memory/math/gemm/copy ops on
+    every component, so we can see how long tl.load vs tl.store vs
+    tl.send actually took at the engine level.
+    """
+    op_logger = getattr(engine, "_op_logger", None)
+    if op_logger is None:
+        print(f"  ({label}) op_logger not available")
+        return
+    src_prefix = f"sip{src_sip}.cube{src_cube}.pe{src_pe}."
+    recs = [r for r in op_logger.records if r.component_id.startswith(src_prefix)]
+    print(f"  ({label}) op_logger records on SRC PE ({src_prefix}*):")
+    for r in recs[:40]:
+        dur = r.t_end - r.t_start
+        comp_short = r.component_id.replace(src_prefix, "")
+        params_short = ""
+        if "nbytes" in r.params:
+            params_short = f" nbytes={r.params['nbytes']}"
+        if "src_addr" in r.params:
+            params_short += f" src_addr={r.params['src_addr']}"
+        if "dst_addr" in r.params:
+            params_short += f" dst_addr={r.params['dst_addr']}"
+        print(
+            f"    t=[{r.t_start:7.2f}..{r.t_end:7.2f}] dur={dur:6.2f}ns  "
+            f"{comp_short:<25s} {r.op_kind:<8s} {r.op_name:<12s}{params_short}"
+        )
+
+
+def _run_raw():
+    captured, restore = _install_per_pe_capture()
+    try:
+        topo = resolve_topology(str(TOPOLOGY_PATH))
+        engine = GraphEngine(topo.topology_obj, enable_data=True)
+        spec = topo.topology_obj.spec
+
+        src_sip, src_cube, src_pe = SRC
+        dst_sip, dst_cube, dst_pe = DST
+        assert src_sip == dst_sip
+
+        src_off = (src_cube * N_PES + src_pe) * N_ELEM * ELEM_BYTES
+        dst_off = (dst_cube * N_PES + dst_pe) * N_ELEM * ELEM_BYTES
+
+        with RuntimeContext(
+            engine=engine,
+            target_device=DeviceSelector("all"),
+            correlation_id="diag_raw",
+            spec=spec,
+        ) as rt:
+            dp = DPPolicy(
+                cube="row_wise", pe="column_wise",
+                num_cubes=N_CUBES, num_pes=N_PES,
+            )
+            rt.ahbm.set_device(src_sip)
+            t = rt.zeros(
+                (N_CUBES, N_PES * N_ELEM), dtype="f16",
+                dp=dp, name="raw_tensor",
+            )
+            t.copy_(rt.from_numpy(
+                np.full((N_CUBES, N_PES * N_ELEM), 1.0, dtype=np.float16),
+            ))
+
+            def kernel(t_ptr, n_elem, tl):
+                pe_id = tl.program_id(axis=0)
+                cube_id = tl.program_id(axis=1)
+                if cube_id == src_cube and pe_id == src_pe:
+                    data = tl.load(
+                        t_ptr + src_off, shape=(n_elem,), dtype="f16",
+                    )
+                    tl.store(t_ptr + dst_off, data)
+
+            pending = rt.launch(
+                "diag_raw_kernel", kernel, t, N_ELEM, _defer_wait=True,
+            )
+            for h, _sip, meta in pending:
+                rt.wait(h, _meta=meta)
+
+        # Compute the RAW sub-txn path: src PE_DMA -> dst HBM_CTRL
+        from kernbench.policy.address.phyaddr import PhysAddr
+        ctx = next(iter(engine._components.values())).ctx
+        src_pe_prefix = f"sip{src_sip}.cube{src_cube}.pe{src_pe}"
+        # Resolve dst PA to HBM controller node
+        # The raw store kernel issues DmaWriteCmd on dst VA; in the engine
+        # this is translated via PE_MMU. For diagnostic we approximate
+        # the destination as the dst cube's HBM controller for slice
+        # belonging to dst_pe.
+        # Use the resolver on a constructed PA matching the same memory
+        # slice the kernel writes to.
+        # The tensor is "row_wise" sharded across cubes, so each cube
+        # owns row[cube_id, :], with each PE owning a column slice.
+        # The actual dst PA depends on the AHBM allocator; we read it
+        # via the tensor's shard map.
+        shard_map = getattr(t, "_shard_map", None) or getattr(t, "shard_map", None)
+        # Fallback: query the resolver directly by constructing a PA in
+        # the dst cube's HBM region. If shard_map is unavailable, still
+        # show the breakdown for src-PE-DMA -> first reachable HBM_CTRL
+        # in dst cube.
+        dst_hbm_id = f"sip{dst_sip}.cube{dst_cube}.hbm_ctrl"
+        if dst_hbm_id not in engine._components:
+            # try alternate naming
+            for nid in engine._components.keys():
+                if (
+                    nid.startswith(f"sip{dst_sip}.cube{dst_cube}.")
+                    and "hbm" in nid
+                ):
+                    dst_hbm_id = nid
+                    break
+
+        # find_path() prepends ".pe_dma" to src_pe automatically
+        try:
+            raw_path = ctx.router.find_path(src_pe_prefix, dst_hbm_id)
+        except Exception as e:
+            raw_path = []
+            print(f"  WARN: find_path raw failed: {e}")
+        if not raw_path:
+            # Try other HBM-related node names in dst cube
+            for nid in engine._components.keys():
+                if not nid.startswith(f"sip{dst_sip}.cube{dst_cube}."):
+                    continue
+                if "hbm" not in nid:
+                    continue
+                try:
+                    p = ctx.router.find_path(src_pe_prefix, nid)
+                except Exception:
+                    p = []
+                if p:
+                    raw_path = p
+                    print(f"  (fallback raw dst node: {nid})")
+                    break
+
+        return captured, ctx, raw_path, engine
+    finally:
+        restore()
+
+
+# ── IPCQ path scenario ───────────────────────────────────────────────
+
+
+def _run_ipcq():
+    captured, restore = _install_per_pe_capture()
+    dst_pe_ipcq_id = (
+        f"sip{DST[0]}.cube{DST[1]}.pe{DST[2]}.pe_ipcq"
+    )
+    arrival_log, restore_arrival = _install_meta_arrival_capture(
+        dst_pe_ipcq_id,
+    )
+    recv_log, restore_recv = _install_recv_capture(dst_pe_ipcq_id)
+    barrier_log, restore_barrier = _install_barrier_capture()
+    try:
+        topo = resolve_topology(str(TOPOLOGY_PATH))
+        engine = GraphEngine(topo.topology_obj, enable_data=True)
+        spec = topo.topology_obj.spec
+
+        src_sip, src_cube, src_pe = SRC
+        dst_sip, dst_cube, dst_pe = DST
+
+        cfg = load_ccl_config()
+        merged = resolve_algorithm_config(cfg, name="intercube_allreduce")
+        merged["slot_size"] = max(int(merged.get("slot_size", 4096)), NBYTES)
+
+        with RuntimeContext(
+            engine=engine,
+            target_device=DeviceSelector("all"),
+            correlation_id="diag_ipcq",
+            spec=spec,
+        ) as rt:
+            configure_sfr_intercube_multisip(engine, spec, merged)
+            dp = DPPolicy(
+                cube="row_wise", pe="column_wise",
+                num_cubes=N_CUBES, num_pes=N_PES,
+            )
+
+            def kernel(t_ptr, n_elem, tl):
+                pe_id = tl.program_id(axis=0)
+                cube_id = tl.program_id(axis=1)
+                if cube_id == src_cube and pe_id == src_pe:
+                    data = tl.load(t_ptr, shape=(n_elem,), dtype="f16")
+                    tl.send(dir=("E" if HOP == "h3" else "S"), src=data)
+                elif cube_id == dst_cube and pe_id == dst_pe:
+                    tl.recv(
+                        dir=("W" if HOP == "h3" else "N"),
+                        shape=(n_elem,), dtype="f16",
+                    )
+
+            tensors = []
+            for s in sorted({src_sip, dst_sip}):
+                rt.ahbm.set_device(s)
+                t = rt.zeros(
+                    (N_CUBES, N_PES * N_ELEM), dtype="f16",
+                    dp=dp, name=f"sip{s}",
+                )
+                t.copy_(rt.from_numpy(
+                    np.full((N_CUBES, N_PES * N_ELEM), 1.0, dtype=np.float16),
+                ))
+                tensors.append(t)
+
+            all_pending = []
+            for tt in tensors:
+                pending = rt.launch(
+                    "diag_ipcq_kernel", kernel, tt, N_ELEM, _defer_wait=True,
+                )
+                all_pending.extend(pending)
+            for h, _sip, meta in all_pending:
+                rt.wait(h, _meta=meta)
+
+        ctx = next(iter(engine._components.values())).ctx
+        src_pe_prefix = f"sip{src_sip}.cube{src_cube}.pe{src_pe}"
+        dst_pe_dma = f"sip{dst_sip}.cube{dst_cube}.pe{dst_pe}.pe_dma"
+        try:
+            ipcq_path = ctx.router.find_path(src_pe_prefix, dst_pe_dma)
+        except Exception as e:
+            ipcq_path = []
+            print(f"  WARN: find_path ipcq failed: {e}")
+        # Snapshot DST PE_IPCQ qp state at end-of-run so we can see what
+        # peer_head_cache/my_tail looked like (and at which directions).
+        qp_state = _snapshot_qp_state(engine, dst_pe_ipcq_id)
+        return (captured, ctx, ipcq_path, engine,
+                arrival_log, qp_state, recv_log, barrier_log)
+    finally:
+        restore_barrier()
+        restore_recv()
+        restore_arrival()
+        restore()
+
+
+# ── Test entry ───────────────────────────────────────────────────────
+
+
+@pytest.mark.diagnostic
+def test_pe_to_pe_diagnostic_h3():
+    print("\n" + "=" * 78)
+    print(f"  Diagnostic: h3 inter-cube horizontal, nbytes={NBYTES}")
+    print(f"  src={SRC}  dst={DST}")
+    print("=" * 78)
+
+    # ── RAW scenario
+    print("\n[RAW] tl.load + tl.store (sender pays both legs)")
+    raw_per_pe, raw_ctx, raw_path, raw_engine = _run_raw()
+    print(f"  per-PE pe_exec_ns ({len(raw_per_pe)} entries):")
+    src_id = f"sip{SRC[0]}.cube{SRC[1]}.pe{SRC[2]}.pe_cpu"
+    dst_id = f"sip{DST[0]}.cube{DST[1]}.pe{DST[2]}.pe_cpu"
+    for nid in (src_id, dst_id):
+        if nid in raw_per_pe:
+            print(f"    {nid:<60s}  {raw_per_pe[nid]:.2f} ns  <-- key PE")
+    nonzero = {k: v for k, v in raw_per_pe.items() if v > 0.5}
+    if nonzero:
+        print(f"  other PEs with pe_exec_ns > 0.5 ns:")
+        for nid, v in sorted(nonzero.items(), key=lambda kv: -kv[1])[:6]:
+            if nid not in (src_id, dst_id):
+                print(f"    {nid:<60s}  {v:.2f} ns")
+    print(f"  max(pe_exec_ns) = "
+          f"{max(raw_per_pe.values()) if raw_per_pe else 0:.2f} ns")
+
+    if raw_path:
+        br = _path_breakdown(raw_ctx, raw_path, NBYTES)
+        _print_breakdown("RAW sub-txn path (src.pe_dma -> dst.hbm_ctrl)", br)
+    _dump_src_op_records(raw_engine, *SRC, "RAW")
+
+    # ── IPCQ scenario
+    print("\n[IPCQ] tl.send + tl.recv (recv pays inbound traversal+drain)")
+    (ipcq_per_pe, ipcq_ctx, ipcq_path, ipcq_engine,
+     arrival_log, qp_state, recv_log, barrier_log) = _run_ipcq()
+    print(f"\n  [BARRIER LOG] {len(barrier_log)} _execute_kernel entries:")
+    src_id = f"sip{SRC[0]}.cube{SRC[1]}.pe{SRC[2]}.pe_cpu"
+    dst_id = f"sip{DST[0]}.cube{DST[1]}.pe{DST[2]}.pe_cpu"
+    n_skipped = 0
+    src_entry = None
+    dst_entry = None
+    for e in barrier_log:
+        if e["barrier_skipped"]:
+            n_skipped += 1
+        if e["node_id"] == src_id:
+            src_entry = e
+        if e["node_id"] == dst_id:
+            dst_entry = e
+    print(f"    PEs entering _execute_kernel: {len(barrier_log)}")
+    print(f"    PEs that SKIPPED barrier (env.now > target_start): {n_skipped}")
+    if src_entry:
+        print(
+            f"    SRC pe ({src_id}): entry_now={src_entry['entry_now']:.2f}  "
+            f"target_start={src_entry['target_start_ns']:.2f}  "
+            f"skipped={src_entry['barrier_skipped']}  "
+            f"late_ns={src_entry['delta_late_ns']:.2f}"
+        )
+    if dst_entry:
+        print(
+            f"    DST pe ({dst_id}): entry_now={dst_entry['entry_now']:.2f}  "
+            f"target_start={dst_entry['target_start_ns']:.2f}  "
+            f"skipped={dst_entry['barrier_skipped']}  "
+            f"late_ns={dst_entry['delta_late_ns']:.2f}"
+        )
+    # Top 5 latest arrivals
+    sorted_late = sorted(
+        [e for e in barrier_log if e["delta_late_ns"] is not None],
+        key=lambda e: -e["delta_late_ns"],
+    )[:5]
+    print(f"    Top 5 latest PE arrivals (positive = barrier missed):")
+    for e in sorted_late:
+        if e["delta_late_ns"] > 0:
+            print(
+                f"      {e['node_id']}: late by {e['delta_late_ns']:.2f} ns  "
+                f"(entry={e['entry_now']:.2f}, target={e['target_start_ns']:.2f})"
+            )
+    print(f"\n  [RECV LOG on dst pe_ipcq] {len(recv_log)} entries:")
+    for e in recv_log:
+        print(
+            f"    {e['phase']:5s} t={e['t']:8.2f} ns  "
+            f"dir={e['direction']}  "
+            f"peer_head_cache={e['peer_head_cache']}  "
+            f"my_tail={e['my_tail']}"
+        )
+    print(f"\n  [META-ARRIVAL LOG on dst pe_ipcq] {len(arrival_log)} arrivals:")
+    for i, e in enumerate(arrival_log):
+        print(
+            f"    #{i:2d} t={e['t']:8.2f} ns  "
+            f"src=(sip{e['src_sip']},cube{e['src_cube']},pe{e['src_pe']}) "
+            f"dir={e['src_direction']}  "
+            f"sender_seq={e['sender_seq']} "
+            f"matched_dir={e['matched_direction']} "
+            f"nbytes={e['nbytes']}"
+        )
+        for d, ph in e["peer_head_cache_before"].items():
+            mt = e["my_tail_before"][d]
+            if ph != 0 or mt != 0 or d == e["matched_direction"]:
+                print(
+                    f"        before: dir={d}  peer_head_cache={ph}  my_tail={mt}"
+                )
+    print(f"\n  [QP STATE END-OF-RUN on dst pe_ipcq]:")
+    for d, st in qp_state.items():
+        print(
+            f"    dir={d}  peer_head_cache={st['peer_head_cache']}  "
+            f"my_tail={st['my_tail']}  rx_range=[{st['rx_range'][0]}..."
+            f"{st['rx_range'][1]})  n_slots={st['n_slots']} "
+            f"slot_size={st['slot_size']}"
+        )
+    print(f"  per-PE pe_exec_ns ({len(ipcq_per_pe)} entries):")
+    for nid in (src_id, dst_id):
+        if nid in ipcq_per_pe:
+            print(f"    {nid:<60s}  {ipcq_per_pe[nid]:.2f} ns  <-- key PE")
+    nonzero = {k: v for k, v in ipcq_per_pe.items() if v > 0.5}
+    if nonzero:
+        print(f"  other PEs with pe_exec_ns > 0.5 ns:")
+        for nid, v in sorted(nonzero.items(), key=lambda kv: -kv[1])[:6]:
+            if nid not in (src_id, dst_id):
+                print(f"    {nid:<60s}  {v:.2f} ns")
+    print(f"  max(pe_exec_ns) = "
+          f"{max(ipcq_per_pe.values()) if ipcq_per_pe else 0:.2f} ns")
+
+    if ipcq_path:
+        br = _path_breakdown(ipcq_ctx, ipcq_path, NBYTES)
+        _print_breakdown("IPCQ sub-txn path (src.pe_dma -> peer.pe_dma)", br)
+    _dump_src_op_records(ipcq_engine, *SRC, "IPCQ")
+    _dump_src_op_records(ipcq_engine, *DST, "IPCQ DST")
+
+    # ── Credit-return path analysis (where the missing IPCQ "ack" lives)
+    print("\n" + "-" * 78)
+    print("Credit-return path (current modeling)")
+    print("-" * 78)
+    src_pe_prefix = f"sip{SRC[0]}.cube{SRC[1]}.pe{SRC[2]}"
+    dst_pe_prefix = f"sip{DST[0]}.cube{DST[1]}.pe{DST[2]}"
+    # PE_IPCQ._credit_latency_ns calls
+    #     ctx.router.find_path(self._pe_prefix, peer_pe_prefix)
+    # where the *destination* lacks the ".pe_dma" suffix. find_path()
+    # only auto-appends to the source, so this raises -> the except
+    # clause silently returns 0.0. Effectively credit latency = 0.
+    try:
+        ipcq_ctx.router.find_path(dst_pe_prefix, src_pe_prefix)
+        bug_caught = False
+    except Exception as e:
+        bug_caught = True
+        print(f"  CONFIRMED BUG in _credit_latency_ns: dest lacks '.pe_dma' "
+              f"-> find_path raises -> caught exception -> returns 0.0")
+        print(f"  Error: {e}")
+    # The intended credit path is recv -> sender (reverse data direction)
+    try:
+        credit_path = ipcq_ctx.router.find_path(
+            dst_pe_prefix, f"{src_pe_prefix}.pe_dma",
+        )
+    except Exception as e:
+        credit_path = []
+        print(f"  WARN: corrected find_path credit failed: {e}")
+    if credit_path:
+        credit_size = 16  # PE_IPCQ default _credit_size_bytes
+        # Today's modeling: drain only, 16 bytes -> ~0.125 ns
+        cur = ipcq_ctx.compute_drain_ns(credit_path, credit_size)
+        # Proposed modeling: full path latency (edges + node overhead + drain)
+        proposed = ipcq_ctx.compute_path_latency_ns(credit_path, credit_size)
+        print(f"  credit path nodes = {len(credit_path)} (recv -> sender)")
+        for nid in credit_path[:6]:
+            print(f"    {nid}")
+        if len(credit_path) > 6:
+            print(f"    ... {len(credit_path) - 6} more nodes")
+        br = _path_breakdown(ipcq_ctx, credit_path, credit_size)
+        print(f"  edge_total_ns       = {br['edge_total_ns']:.2f}")
+        print(f"  overhead_total_ns   = {br['overhead_total_ns']:.2f}")
+        print(f"  drain_ns(16 bytes)  = {br['drain_ns']:.2f}")
+        print(f"  CURRENT _credit_latency_ns (drain only) = {cur:.3f} ns")
+        print(f"  PROPOSED            (compute_path_latency_ns) = {proposed:.2f} ns")
+        print(f"  delta               = {proposed - cur:+.2f} ns")
+
+    # ── Comparison summary
+    print("\n" + "-" * 78)
+    print("Summary")
+    print("-" * 78)
+    raw_max = max(raw_per_pe.values()) if raw_per_pe else 0.0
+    ipcq_max = max(ipcq_per_pe.values()) if ipcq_per_pe else 0.0
+    print(f"  RAW  max(pe_exec_ns)              = {raw_max:.2f} ns")
+    print(f"  IPCQ max(pe_exec_ns) (current)    = {ipcq_max:.2f} ns")
+    print(f"  delta (RAW - IPCQ current)        = {raw_max - ipcq_max:+.2f} ns")
+    if credit_path:
+        ipcq_with_credit = ipcq_max + (proposed - cur)
+        print(
+            f"  IPCQ projected w/ blocking credit + full path overhead "
+            f"= {ipcq_with_credit:.2f} ns"
+        )
+        print(
+            f"  delta (RAW - IPCQ projected)      = "
+            f"{raw_max - ipcq_with_credit:+.2f} ns  "
+            f"(<= 0 means IPCQ >= RAW)"
+        )
+
+    # No assertions — this is observational.
+    assert raw_per_pe, "no RAW pe_exec_ns recorded"
+    assert ipcq_per_pe, "no IPCQ pe_exec_ns recorded"

tests/test_sip_topology_rectangular.py

@@ -0,0 +1,106 @@
+"""Rectangular (non-square) SIP-level 2D topology support.
+
+Phase 1 regression target: today the 2D builtin topology functions in
+``kernbench.ccl.topologies`` (``mesh_2d``, ``torus_2d``,
+``mesh_2d_no_wrap``) hardcode ``side = sqrt(world_size)`` and raise
+``ValueError`` for any non-square ``world_size``. This blocks running
+the allreduce sweep at n_sips=6 on torus/mesh layouts.
+
+Phase 2 will extend these functions to accept optional ``w, h`` kwargs
+so a 2×3 (or 3×2, etc.) layout works. Until then, every test below is
+expected to FAIL.
+
+Layout convention used here (matches non-rectangular case):
+    rank = row * w + col   for 0 <= row < h, 0 <= col < w
+
+For w=2, h=3, world_size=6 the layout is:
+
+         col=0  col=1
+  row=0:   0      1
+  row=1:   2      3
+  row=2:   4      5
+"""
+from __future__ import annotations
+
+import pytest
+
+from kernbench.ccl.topologies import (
+    mesh_2d,
+    mesh_2d_no_wrap,
+    torus_2d,
+)
+
+
+# ── mesh_2d_no_wrap (no wrap-around) ──────────────────────────────────
+
+
+def test_mesh_2d_no_wrap_2x3_top_left():
+    """rank 0 (top-left, no N, no W): only S and E."""
+    nbrs = mesh_2d_no_wrap(rank=0, world_size=6, w=2, h=3)
+    assert nbrs == {"S": 2, "E": 1}, nbrs
+
+
+def test_mesh_2d_no_wrap_2x3_top_right():
+    """rank 1 (top-right, no N, no E): only S and W."""
+    nbrs = mesh_2d_no_wrap(rank=1, world_size=6, w=2, h=3)
+    assert nbrs == {"S": 3, "W": 0}, nbrs
+
+
+def test_mesh_2d_no_wrap_2x3_middle_left():
+    """rank 2 (middle-left, no W): N, S, E."""
+    nbrs = mesh_2d_no_wrap(rank=2, world_size=6, w=2, h=3)
+    assert nbrs == {"N": 0, "S": 4, "E": 3}, nbrs
+
+
+def test_mesh_2d_no_wrap_2x3_bottom_right():
+    """rank 5 (bottom-right, no S, no E): only N and W."""
+    nbrs = mesh_2d_no_wrap(rank=5, world_size=6, w=2, h=3)
+    assert nbrs == {"N": 3, "W": 4}, nbrs
+
+
+# ── torus_2d (wrap-around on all four edges) ─────────────────────────
+
+
+def test_torus_2d_2x3_top_left():
+    """rank 0: N wraps to row 2 col 0 (rank 4); W wraps to col 1 (rank 1)."""
+    nbrs = torus_2d(rank=0, world_size=6, w=2, h=3)
+    assert nbrs == {"N": 4, "S": 2, "W": 1, "E": 1}, nbrs
+
+
+def test_torus_2d_2x3_bottom_right():
+    """rank 5: S wraps to row 0 (rank 1); E wraps to col 0 (rank 4)."""
+    nbrs = torus_2d(rank=5, world_size=6, w=2, h=3)
+    assert nbrs == {"N": 3, "S": 1, "W": 4, "E": 4}, nbrs
+
+
+# ── mesh_2d alias for torus_2d ───────────────────────────────────────
+
+
+def test_mesh_2d_2x3_matches_torus_2d():
+    """mesh_2d is currently a torus alias; behaviour must match torus_2d."""
+    for rank in range(6):
+        assert mesh_2d(rank=rank, world_size=6, w=2, h=3) == \
+            torus_2d(rank=rank, world_size=6, w=2, h=3)
+
+
+# ── Back-compat: square layouts still work without w/h kwargs ────────
+
+
+def test_square_back_compat_mesh_2d_no_wrap():
+    """Calling without w, h should still work for square world_size."""
+    nbrs = mesh_2d_no_wrap(rank=0, world_size=4)
+    assert nbrs == {"S": 2, "E": 1}, nbrs
+
+
+def test_square_back_compat_torus_2d():
+    nbrs = torus_2d(rank=0, world_size=4)
+    assert nbrs == {"N": 2, "S": 2, "W": 1, "E": 1}, nbrs
+
+
+# ── Validation: w*h must match world_size ────────────────────────────
+
+
+def test_rectangular_dims_must_match_world_size():
+    """Phase 2 contract: explicit w, h must satisfy w*h == world_size."""
+    with pytest.raises(ValueError):
+        mesh_2d_no_wrap(rank=0, world_size=6, w=3, h=3)  # 9 != 6