ADR-0009 D5: chain-aware target_start_ns + zero-byte launch fanout

The single-walk predictor (find_node_path(io_cpu, pe_cpu) +
compute_path_latency_ns) under-shot actual dispatch latency for far
cubes -- the routing graph could pick a path bypassing M_CPU, and
non-zero-nbytes launch sub-txns serialized on shared first hops.
Far PEs arrived at _execute_kernel after target_start_ns, silently
skipped the barrier yield, and started pe_exec_start late. Their
reported pe_exec_ns under-counted by exactly the late_ns amount
(63 ns observed at h4 cube4.pe0 in the IPCQ test, up to 113 ns
worst case for cubes 9-11), producing the suspicious flat region
in the h4 IPCQ curve at 8192/10240 bytes.

Fix:
  - IO_CPU predictor uses the explicit two-leg chain
    (IO_CPU->M_CPU + M_CPU->PE_CPU - io.overhead - m.overhead), so
    every PE on every targeted cube has a barrier >= its real
    dispatch arrival.
  - Kernel-launch fanout sub-txns carry nbytes=0 (control-plane,
    not data-plane), removing the per-cube fanout serialization
    that pushed far M_CPUs past the predictor.
  - Legacy io_cpu mirror updated.

ADR-0009 D5 mechanism updated to specify the two-leg formula and
the nbytes=0 requirement. New tests/test_d5_barrier_invariant.py
asserts (a) no PE enters _execute_kernel after target_start_ns and
(b) every PE in a multi-cube launch has identical pe_exec_start --
both regressions silently pass on the existing
tests/test_kernel_launch_sync.py because that test only inspects
post-aggregation max(pe_exec_ns).

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`
+  computes `target_start_ns = env.now + max_latency` where
-  is the maximum `ComponentContext.compute_path_latency_ns(path)` across
+  `max_latency` is the maximum, over every target (sip, cube, pe)
-  every target (sip, cube, pe) tuple — `path = find_node_path(io_cpu,
+  tuple, of the **two-leg dispatch chain**:
-  pe_cpu_id)`. The stamped value is placed on the request carried by
+
-  every fanned-out sub-Transaction.
+  ```
+  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

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(
+                        m_to_pe_path = self.ctx.router.find_node_path(
-                            self.node.id, pe_cpu_id,
+                            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(
+                    leg2 = self.ctx.compute_path_latency_ns(
-                        path, nbytes=0,
+                        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/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(
+                        m_to_pe_path = self.ctx.router.find_node_path(
-                            self.node.id, pe_cpu_id,
+                            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(
+                    leg2 = self.ctx.compute_path_latency_ns(
-                        path, nbytes=0,
+                        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/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}"
+        )