Kernel-launch sync (ADR-0009 D5) and IPCQ drain at inbound (ADR-0023)

- KernelLaunchMsg gains target_start_ns: IO_CPU stamps a global barrier
  (max path latency across every target PE), M_CPU passes it through,
  PE_CPU yields until it before recording pe_exec_start. Every PE in a
  launch begins kernel execution at the same env.now regardless of its
  dispatch path length — eliminates per-PE dispatch-offset artifact in
  cross-PE and cross-cube latency measurements.

- PE_DMA._handle_ipcq_inbound now pays Transaction.drain_ns at the top,
  matching the terminal-drain behavior of ComponentBase._forward_txn for
  every non-IPCQ Transaction. SRC-side tl.send stays fire-and-forget
  (sender doesn't yield on sub_done); tl.recv now blocks until bytes
  have actually drained into its inbox.

- ComponentContext: new compute_path_latency_ns helper + node_overhead_ns
  field populated by GraphEngine.

- tests/test_kernel_launch_sync.py: asserts all PEs in one launch
  produce identical pe_exec_ns for a no-op kernel (zero spread).

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

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

@@ -67,6 +67,51 @@ Completion semantics:
 
 ---
 
+### D5. Launch timing is endpoint-synchronized
+
+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
+from the launch entry point.
+
+Rationale. The dispatch tree Host → IO_CPU → M_CPU → PE_CPU has variable
+latency at every level. PEs near their M_CPU receive the launch earlier
+than PEs farther away; cubes near an IO_CPU receive it earlier than cubes
+farther away. Without synchronization, each PE's kernel begins at a
+different `env.now`, making per-PE metrics such as `pe_exec_ns` a function
+of dispatch-path geometry rather than of the kernel's behavior —
+producing measurement artifacts in benchmarks that time kernel-internal
+waits (for example `tl.recv` on cross-cube or cross-SIP hops).
+
+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.
+- **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
+  `env.now + max(local command-path latency)`.
+- **PE_CPU** yields `env.timeout(target_start_ns - env.now)` at the top
+  of `_execute_kernel`, before recording `pe_exec_start` and invoking
+  the kernel body.
+- When `target_start_ns is None`, PE_CPU falls through to the legacy
+  unsynchronized behavior — preserving backward compatibility.
+
+IO_CPU-level stamping guarantees every PE across every targeted cube
+uses the same barrier sim-time, eliminating both the within-cube
+dispatch-offset artifact *and* the cross-cube offset artifact in
+multi-cube launches. Models a real-hardware timed-broadcast launch
+(latency-equalized dispatch tree).
+
+The synchronization is internal to the engine / IO_CPU / M_CPU / PE_CPU
+control plane — runtime API and application kernels are unchanged.
+
+---
+
 ## Links
 
 - SPEC R1, R2, R7, R8

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

@@ -420,11 +420,21 @@ fan-out (see `IpcqInitMsg` in D12).
 #### PE_DMA's added responsibility
 
 When `vc_comm` receives a token, PE_DMA processes it as the following
-**atomic** sequence. **No SimPy yield is allowed between the two steps**
+sequence: pay the Transaction's terminal BW drain, then atomically
-(invariant I6):
+write data and forward metadata. **No SimPy yield is allowed between
+the data write and the metadata forward** (invariant I6). The drain
+yield must sit before the atomic block, not inside it:
 
 ```python
-def _on_vc_comm_recv(self, env, token):
+def _on_vc_comm_recv(self, env, txn):
+    # Pay the terminal BW drain (nbytes / bottleneck_bw stamped by the
+    # sender PE_DMA). MUST happen before the atomic block so recv only
+    # wakes after the bytes have "landed".
+    drain = getattr(txn, "drain_ns", 0.0)
+    if drain > 0:
+        yield env.timeout(drain)
+
+    token = txn.request
     # ── ATOMIC: no yield between these two operations ──
     data = self._memory_store.read(token.src_space, token.src_addr,
                                    shape=..., dtype=...)
@@ -439,6 +449,33 @@ The final `put` is yieldable but uses an unbounded internal store, so
 it completes in a single step. That `put` is the closing call of the
 atomic block; nothing may be inserted before it.
 
+#### Drain-at-inbound semantics (D9 timing model)
+
+The Transaction carries `drain_ns = nbytes / bottleneck_bw_on_path`
+stamped at send-side PE_DMA. In this simulator per-hop `overhead_ns`
+is paid at each forwarding component via `run()`, and the remaining
+BW drain is paid once at the Transaction's terminal. Every non-IPCQ
+Transaction (raw DMA, kernel-launch fanout, etc.) pays this drain via
+`ComponentBase._forward_txn` at the terminal node. For IPCQ the
+destination PE_DMA intercepts the Transaction with `_handle_ipcq_inbound`
+(so IPCQ-specific data write + metadata forward can happen), so **the
+drain MUST be paid explicitly at the top of that handler** to keep
+IPCQ's timing model on par with every other fabric Transaction.
+
+Side-effects of paying drain here:
+
+- **SRC `tl.send`** is unchanged — fire-and-forget semantics are
+  preserved because the sender PE_DMA does not `yield sub_done`. The
+  `sub_done.succeed()` call (made after metadata forward below) is an
+  event with no listener on the sender side.
+- **DST `tl.recv`** unblocks `drain_ns` later. Since recv wakes only
+  when `IpcqMetaArrival` reaches its local PE_IPCQ, and the metadata
+  forward now happens after the drain, recv observes the full fabric
+  transfer time including bandwidth cost.
+
+Matches the physical picture: send dispatches and leaves; recv waits
+until the bytes have actually been drained into its inbox.
+
 ### D9.5. ADR-0020 (2-pass) integration
 
 `tl.send` / `tl.recv` integrates with ADR-0020's two-pass model. Phase

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

@@ -426,11 +426,22 @@ backend init에서 IpcqInitMsg fan-out 시 양방향 fast path channel을 함께
 
 #### PE_DMA의 책임 추가
 
-PE_DMA(vc_comm)는 token 수신 시 다음 atomic 시퀀스로 처리한다.
+PE_DMA(vc_comm)는 token 수신 시 다음 시퀀스로 처리한다: Transaction
-**두 동작 사이에 SimPy yield를 두어서는 안 된다** (I6 MUST 규칙 참조):
+terminal의 BW drain을 먼저 지불하고, 이어서 atomic하게 data write +
+metadata forward 수행. **data write와 metadata forward 사이에는 SimPy
+yield를 두어서는 안 된다** (I6 MUST 규칙 참조). drain yield는 atomic
+구간 안이 아니라 그 앞에 위치해야 한다:
 
 ```python
-def _on_vc_comm_recv(self, env, token):
+def _on_vc_comm_recv(self, env, txn):
+    # Sender PE_DMA가 찍어 둔 drain_ns (= nbytes / bottleneck_bw) 를
+    # 여기서 지불. atomic 구간보다 앞이어야 한다 — recv는 bytes가
+    # "도착"한 이후에만 깨어나야 하므로.
+    drain = getattr(txn, "drain_ns", 0.0)
+    if drain > 0:
+        yield env.timeout(drain)
+
+    token = txn.request
     # ── ATOMIC: 두 동작 사이에 yield 금지 ──
     # 1. data를 dst_addr에 write (dst의 메모리 공간은 token.dst_endpoint.buffer_kind)
     data = self._memory_store.read(token.src_space, token.src_addr,
@@ -446,6 +457,32 @@ wire로 capacity가 unbounded인 store를 사용하므로 즉시 완료된다 (
 single-step). 이 최종 put이 atomic 구간의 끝이며, 그 이전에 다른 yield가
 삽입되면 안 된다.
 
+#### Drain-at-inbound semantics (D9 timing model)
+
+Transaction은 sender PE_DMA가 `drain_ns = nbytes / bottleneck_bw_on_path`
+를 찍어 둔 상태로 fabric에 들어간다. 이 simulator에서 per-hop `overhead_ns`
+는 각 forwarding component의 `run()` 에서 지불되고, 남은 BW drain은
+Transaction의 terminal node에서 한 번 지불된다. IPCQ가 아닌 모든
+Transaction (raw DMA, kernel-launch fanout 등) 은
+`ComponentBase._forward_txn` 이 terminal에서 이 drain을 지불한다. IPCQ의
+경우 목적지 PE_DMA가 `_handle_ipcq_inbound` 핸들러로 Transaction을
+가로채서 (IPCQ 전용 data write + metadata forward를 해야 하므로)
+**이 핸들러 최상단에서 drain을 명시적으로 지불해야 한다** — 그래야 IPCQ의
+timing model이 다른 모든 fabric Transaction과 동일선상에 놓인다.
+
+여기서 drain을 지불할 때의 side-effect:
+
+- **SRC `tl.send`**: 동작 불변. sender PE_DMA가 `sub_done` 을 `yield`
+  하지 않으므로 fire-and-forget 의미가 보존된다. metadata forward 이후
+  호출되는 `sub_done.succeed()` 는 sender 입장에서 listener가 없는 이벤트.
+- **DST `tl.recv`**: `drain_ns` 만큼 늦게 깨어난다. recv는 local PE_IPCQ
+  의 `IpcqMetaArrival` 수신 시에만 wake되며, metadata forward가 drain
+  이후로 이동했으므로 recv는 bandwidth까지 포함한 전체 fabric transfer
+  시간을 관측하게 된다.
+
+물리적 그림과 일치: send는 dispatch하고 바로 반환; recv는 bytes가 실제로
+자신의 inbox로 drain될 때까지 대기.
+
 #### Backpressure latency 정확도
 
 backpressure 해제까지 걸리는 시간:

src/kernbench/components/builtin/io_cpu.py

@@ -58,7 +58,18 @@ class IoCpuComponent(ComponentBase):
             self._pending[key] = (expected, received, parent_done)
 
     def _dispatch_to_m_cpus(self, env: simpy.Environment, txn: Any) -> Generator:
-        """Fan out sub-Transactions to target cube M_CPUs, wait for responses."""
+        """Fan out sub-Transactions to target cube M_CPUs, wait for responses.
+
+        ADR-0009 D5 (extended): for KernelLaunchMsg, stamp a single global
+        target_start_ns = env.now + max(IO_CPU → any target PE_CPU path
+        latency across all target cubes). M_CPU passes this value through
+        unchanged; every PE in every cube yields until the same sim-time
+        before beginning kernel execution. Without this, cross-cube
+        launches would have each cube's M_CPU compute its own per-cube
+        barrier relative to its local env.now, leaving PEs on different
+        cubes out of sync (the "h3/h4 dispatch-offset artifact").
+        """
+        import dataclasses
         from kernbench.runtime_api.kernel import KernelLaunchMsg, MemoryReadMsg, MemoryWriteMsg
 
         request = txn.request
@@ -72,6 +83,36 @@ class IoCpuComponent(ComponentBase):
             txn.done.succeed()
             return
 
+        # For KernelLaunchMsg, compute the global barrier once here so
+        # every downstream PE_CPU uses the same target_start_ns.
+        if isinstance(request, KernelLaunchMsg):
+            global_max_latency = 0.0
+            pe_ids = self._resolve_pe_ids(
+                getattr(request, "target_pe", "all")
+            )
+            for sip, cube in cube_targets:
+                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,
+                        )
+                    except Exception:
+                        continue
+                    if len(path) < 2:
+                        continue
+                    latency = self.ctx.compute_path_latency_ns(
+                        path, nbytes=0,
+                    )
+                    if latency > global_max_latency:
+                        global_max_latency = latency
+            request = dataclasses.replace(
+                request,
+                target_start_ns=float(env.now) + global_max_latency,
+            )
+
         # Setup aggregation
         self._pending[request.request_id] = (len(cube_targets), 0, txn.done)
 
@@ -91,6 +132,19 @@ class IoCpuComponent(ComponentBase):
             )
             yield self.out_ports[path[1]].put(sub_txn.advance())
 
+    def _resolve_pe_ids(self, target_pe: Any) -> list[int]:
+        """Resolve target_pe → list of PE indices (mirrors M_CPU logic)."""
+        if isinstance(target_pe, int):
+            return [target_pe]
+        if isinstance(target_pe, tuple):
+            return list(target_pe)
+        # "all": all PEs in a cube
+        n_slices = 8
+        if self.ctx and self.ctx.spec:
+            mm = self.ctx.spec.get("cube", {}).get("memory_map", {})
+            n_slices = mm.get("hbm_slices_per_cube", 8)
+        return list(range(n_slices))
+
     def _resolve_cube_targets(self, request: Any) -> list[tuple[int, int]]:
         """Return list of (sip, cube) pairs to fan out to."""
         from kernbench.runtime_api.kernel import (

src/kernbench/components/builtin/m_cpu.py

@@ -162,7 +162,11 @@ class MCpuComponent(ComponentBase):
         Routes through find_node_path (M_CPU → NOC → PE_CPU command edges).
         PE_CPU sends ResponseMsg back via NOC → M_CPU on completion.
         Then sends aggregate ResponseMsg back to IO_CPU on the reverse path.
+
+        ADR-0009 D5: stamps target_start_ns so every PE in this fanout
+        starts executing at the same env.now regardless of dispatch path.
         """
+        import dataclasses
         request = txn.request
         target_pe = getattr(request, "target_pe", "all")
         cube_prefix = self.node.id.rsplit(".", 1)[0]  # e.g. "sip0.cube0"
@@ -172,9 +176,13 @@ class MCpuComponent(ComponentBase):
             txn.done.succeed()
             return
 
-        # Fan out to each PE_CPU, using response-based aggregation
+        # Resolve per-PE paths. If IO_CPU already stamped a global
-        sub_txns: list[Transaction] = []
+        # target_start_ns (ADR-0009 D5 extended), pass it through
-        n_dispatched = 0
+        # unchanged so every PE across every cube uses the same barrier.
+        # Otherwise (e.g. direct-to-M_CPU launch in a unit test) compute
+        # a per-cube barrier from env.now.
+        per_pe: list[tuple[int, list[str], float]] = []
+        max_latency = 0.0
         for pe_id in pe_ids:
             pe_cpu_id = f"{cube_prefix}.pe{pe_id}.pe_cpu"
             try:
@@ -183,8 +191,24 @@ class MCpuComponent(ComponentBase):
                 continue
             if len(path) < 2:
                 continue
+            latency = self.ctx.compute_path_latency_ns(path, nbytes=0)
+            per_pe.append((pe_id, path, latency))
+            if latency > max_latency:
+                max_latency = latency
+
+        if getattr(request, "target_start_ns", None) is not None:
+            stamped_request = request
+        else:
+            stamped_request = dataclasses.replace(
+                request, target_start_ns=float(env.now) + max_latency,
+            )
+
+        # Fan out to each PE_CPU, using response-based aggregation
+        sub_txns: list[Transaction] = []
+        n_dispatched = 0
+        for pe_id, path, _lat in per_pe:
             sub_txn = Transaction(
-                request=request, path=path, step=0,
+                request=stamped_request, path=path, step=0,
                 nbytes=0, done=env.event(),
             )
             yield self.out_ports[path[1]].put(sub_txn.advance())

src/kernbench/components/builtin/pe_cpu.py

@@ -95,6 +95,13 @@ class PeCpuComponent(ComponentBase):
         request = txn.request
         yield from self.run(env, 0)
 
+        # ADR-0009 D5: synchronized launch barrier. If M_CPU stamped a
+        # target_start_ns, wait until then so every PE in this launch
+        # begins pe_exec measurement at the same simulated time.
+        target_start = getattr(request, "target_start_ns", None)
+        if target_start is not None and target_start > env.now:
+            yield env.timeout(float(target_start) - env.now)
+
         kernel_fn = get_kernel(request.kernel_ref.name)
         num_programs = self._derive_num_programs(request)
         kernel_args = self._unpack_kernel_args(request)

src/kernbench/components/builtin/pe_dma.py

@@ -186,13 +186,37 @@ class PeDmaComponent(PeEngineBase):
     # ── IPCQ inbound (fabric → PE_DMA → MemoryStore + PE_IPCQ) ──────
 
     def _handle_ipcq_inbound(self, env: simpy.Environment, txn: Any) -> Generator:
-        """At destination PE_DMA: atomically write data and forward metadata.
+        """At destination PE_DMA: pay terminal drain, then atomically write
+        data and forward metadata.
+
+        ADR-0023 D9 (drain at inbound terminal): the Transaction carries
+        ``drain_ns = nbytes / bottleneck_bw_on_path`` stamped by the sender
+        PE_DMA. Like every other Transaction terminal in the simulator (see
+        ``ComponentBase._forward_txn``), this drain must be paid when the
+        Transaction reaches its destination. SRC-side ``tl.send`` is
+        fire-and-forget — it never yields on ``sub_done`` — so paying the
+        drain here does NOT delay the sender. What it DOES delay is the
+        IpcqMetaArrival forwarded below: that delay is the only signal
+        ``tl.recv`` on DST blocks on, which is exactly the desired
+        semantics — "send dispatches and returns; recv waits until the
+        bytes have actually landed in its inbox".
+
+        The drain MUST be paid before the atomic block — inserting a yield
+        inside would break invariant I6.
 
         I6 (MUST): no SimPy yield between MemoryStore.write and the
         IpcqMetaArrival put into PE_IPCQ.
         """
         from kernbench.common.ipcq_types import IpcqMetaArrival
 
+        # Pay terminal BW drain before the atomic write/metadata forward.
+        # Without this, IPCQ effectively got fabric bandwidth for free at
+        # the terminal (only intermediate-hop overhead_ns was charged),
+        # making IPCQ lower than raw DMA at large sizes in benchmarks.
+        drain = getattr(txn, "drain_ns", 0.0)
+        if drain > 0:
+            yield env.timeout(drain)
+
         token = txn.request
 
         # ── ATOMIC: do not introduce yield between these two operations ──

src/kernbench/components/context.py

@@ -26,6 +26,9 @@ class ComponentContext:
     spec: dict = field(default_factory=dict)  # topology spec (cube layout, PE count, etc.)
     memory_store: Any = None  # MemoryStore for Phase 1 data-aware execution (ADR-0020)
     op_logger: Any = None     # OpLogger for Phase 1 op recording (ADR-0020)
+    # node_id -> overhead_ns (ADR-0009 D5: used by M_CPU to compute per-PE
+    # dispatch latency when stamping target_start_ns on KernelLaunchMsg).
+    node_overhead_ns: dict[str, float] = field(default_factory=dict)
 
     def get_shared_resource(
         self, env: simpy.Environment, key: str, capacity: int = 1,
@@ -52,3 +55,19 @@ class ComponentContext:
         if min_bw == float("inf"):
             return 0.0
         return nbytes / min_bw
+
+    def compute_path_latency_ns(self, path: list[str], nbytes: int = 0) -> float:
+        """Formula latency along path: wire + per-node overhead + drain.
+
+        ADR-0009 D5: M_CPU uses this to compute per-PE dispatch latency
+        when stamping target_start_ns on KernelLaunchMsg fanout.
+        """
+        total = 0.0
+        for i in range(len(path) - 1):
+            edge = self.edge_map.get((path[i], path[i + 1]))
+            if edge:
+                total += edge.distance_mm * self.ns_per_mm
+        for node_id in path:
+            total += self.node_overhead_ns.get(node_id, 0.0)
+        total += self.compute_drain_ns(path, nbytes)
+        return total

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

@@ -58,7 +58,13 @@ class IoCpuComponent(ComponentBase):
             self._pending[key] = (expected, received, parent_done)
 
     def _dispatch_to_m_cpus(self, env: simpy.Environment, txn: Any) -> Generator:
-        """Fan out sub-Transactions to target cube M_CPUs, wait for responses."""
+        """Fan out sub-Transactions to target cube M_CPUs, wait for responses.
+
+        ADR-0009 D5 (extended): stamp a global target_start_ns on
+        KernelLaunchMsg so every PE across every target cube starts at
+        the same env.now. See the non-legacy builtin for full rationale.
+        """
+        import dataclasses
         from kernbench.runtime_api.kernel import KernelLaunchMsg, MemoryReadMsg, MemoryWriteMsg
 
         request = txn.request
@@ -72,6 +78,34 @@ class IoCpuComponent(ComponentBase):
             txn.done.succeed()
             return
 
+        if isinstance(request, KernelLaunchMsg):
+            global_max_latency = 0.0
+            pe_ids = self._resolve_pe_ids(
+                getattr(request, "target_pe", "all")
+            )
+            for sip, cube in cube_targets:
+                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,
+                        )
+                    except Exception:
+                        continue
+                    if len(path) < 2:
+                        continue
+                    latency = self.ctx.compute_path_latency_ns(
+                        path, nbytes=0,
+                    )
+                    if latency > global_max_latency:
+                        global_max_latency = latency
+            request = dataclasses.replace(
+                request,
+                target_start_ns=float(env.now) + global_max_latency,
+            )
+
         # Setup aggregation
         self._pending[request.request_id] = (len(cube_targets), 0, txn.done)
 
@@ -91,6 +125,18 @@ class IoCpuComponent(ComponentBase):
             )
             yield self.out_ports[path[1]].put(sub_txn.advance())
 
+    def _resolve_pe_ids(self, target_pe: Any) -> list[int]:
+        """Resolve target_pe → list of PE indices (mirrors M_CPU logic)."""
+        if isinstance(target_pe, int):
+            return [target_pe]
+        if isinstance(target_pe, tuple):
+            return list(target_pe)
+        n_slices = 8
+        if self.ctx and self.ctx.spec:
+            mm = self.ctx.spec.get("cube", {}).get("memory_map", {})
+            n_slices = mm.get("hbm_slices_per_cube", 8)
+        return list(range(n_slices))
+
     def _resolve_cube_targets(self, request: Any) -> list[tuple[int, int]]:
         """Return list of (sip, cube) pairs to fan out to."""
         from kernbench.runtime_api.kernel import (

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

@@ -162,7 +162,11 @@ class MCpuComponent(ComponentBase):
         Routes through find_node_path (M_CPU → NOC → PE_CPU command edges).
         PE_CPU sends ResponseMsg back via NOC → M_CPU on completion.
         Then sends aggregate ResponseMsg back to IO_CPU on the reverse path.
+
+        ADR-0009 D5: stamps target_start_ns so every PE in this fanout
+        starts executing at the same env.now regardless of dispatch path.
         """
+        import dataclasses
         request = txn.request
         target_pe = getattr(request, "target_pe", "all")
         cube_prefix = self.node.id.rsplit(".", 1)[0]  # e.g. "sip0.cube0"
@@ -172,9 +176,10 @@ class MCpuComponent(ComponentBase):
             txn.done.succeed()
             return
 
-        # Fan out to each PE_CPU, using response-based aggregation
+        # Resolve per-PE paths. If IO_CPU already stamped a global
-        sub_txns: list[Transaction] = []
+        # target_start_ns (ADR-0009 D5 extended), pass it through.
-        n_dispatched = 0
+        per_pe: list[tuple[int, list[str], float]] = []
+        max_latency = 0.0
         for pe_id in pe_ids:
             pe_cpu_id = f"{cube_prefix}.pe{pe_id}.pe_cpu"
             try:
@@ -183,8 +188,24 @@ class MCpuComponent(ComponentBase):
                 continue
             if len(path) < 2:
                 continue
+            latency = self.ctx.compute_path_latency_ns(path, nbytes=0)
+            per_pe.append((pe_id, path, latency))
+            if latency > max_latency:
+                max_latency = latency
+
+        if getattr(request, "target_start_ns", None) is not None:
+            stamped_request = request
+        else:
+            stamped_request = dataclasses.replace(
+                request, target_start_ns=float(env.now) + max_latency,
+            )
+
+        # Fan out to each PE_CPU, using response-based aggregation
+        sub_txns: list[Transaction] = []
+        n_dispatched = 0
+        for pe_id, path, _lat in per_pe:
             sub_txn = Transaction(
-                request=request, path=path, step=0,
+                request=stamped_request, path=path, step=0,
                 nbytes=0, done=env.event(),
             )
             yield self.out_ports[path[1]].put(sub_txn.advance())

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

@@ -71,6 +71,13 @@ class PeCpuComponent(ComponentBase):
         request = txn.request
         yield from self.run(env, 0)
 
+        # ADR-0009 D5: synchronized launch barrier. If M_CPU stamped a
+        # target_start_ns, wait until then so every PE in this launch
+        # begins pe_exec measurement at the same simulated time.
+        target_start = getattr(request, "target_start_ns", None)
+        if target_start is not None and target_start > env.now:
+            yield env.timeout(float(target_start) - env.now)
+
         kernel_fn = get_kernel(request.kernel_ref.name)
         num_programs = self._derive_num_programs(request)
         kernel_args = self._unpack_kernel_args(request)

src/kernbench/runtime_api/kernel.py

@@ -90,6 +90,11 @@ class KernelLaunchMsg:
     args: tuple[KernelArg, ...]
     target_cubes: tuple[int, ...] | Literal["all"] = "all"
     target_pe: int | tuple[int, ...] | Literal["all"] = "all"
+    # ADR-0009 D5: synchronized kernel start. When set, each PE_CPU yields
+    # until env.now >= target_start_ns before beginning kernel execution,
+    # so every PE in a launch starts at the same simulated time regardless
+    # of its M_CPU dispatch path length. Stamped by M_CPU fan-out.
+    target_start_ns: float | None = None
     msg_type: Literal["kernel_launch"] = "kernel_launch"
 
 

src/kernbench/sim_engine/engine.py

@@ -67,6 +67,10 @@ class GraphEngine:
             spec=graph.spec,
             memory_store=self._memory_store,
             op_logger=self._op_logger,
+            node_overhead_ns={
+                nid: float(n.attrs.get("overhead_ns", 0.0))
+                for nid, n in graph.nodes.items()
+            },
         )
         self._components: dict[str, ComponentBase] = {
             node_id: ComponentRegistry.create(node, overrides, ctx)

tests/test_kernel_launch_sync.py

@@ -0,0 +1,62 @@
+"""ADR-0009 D5: synchronized launch barrier.
+
+M_CPU stamps KernelLaunchMsg with target_start_ns = env.now + max path
+latency; PE_CPU yields until that time before recording pe_exec_start.
+Every PE in a single launch MUST begin kernel execution at the same
+env.now regardless of its dispatch path length.
+
+We verify this indirectly: for a no-op kernel, pe_exec_ns = env.now -
+pe_exec_start. If every PE's pe_exec_start is identical and every PE
+runs the same no-op body, every pe_exec_ns value must be identical.
+Without D5, pe_exec_start varies by dispatch-path length and so does
+pe_exec_ns.
+"""
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+
+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 test_kernel_launch_sync_all_pes_have_equal_exec_time():
+    """No-op kernel: every PE's pe_exec_ns must be identical under D5."""
+    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="sync_test", 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("sync_probe", kernel, t, 64, _defer_wait=True)
+        for h, _sip, meta in pending:
+            ctx.wait(h, _meta=meta)
+
+        pe_exec_vals = []
+        for h, _sip, _meta in pending:
+            _, trace = engine.get_completion(h)
+            if trace and trace.get("pe_exec_ns") is not None:
+                pe_exec_vals.append(float(trace["pe_exec_ns"]))
+
+    assert pe_exec_vals, "expected completion traces with pe_exec_ns"
+    spread = max(pe_exec_vals) - min(pe_exec_vals)
+    assert spread < 1e-6, (
+        f"ADR-0009 D5 violated: pe_exec_ns spread across PEs = "
+        f"{spread:.6f} ns (expected 0). Values: {pe_exec_vals}"
+    )