Commit Graph

17 Commits

Author SHA1 Message Date
mukesh 14d800b0ae 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>
2026-04-23 15:30:29 -07:00
mukesh 6918e6e906 PE-to-PE latency test + supporting fixes
Adds tests/test_pe_to_pe_latency.py: a sweep that measures PE-to-PE
transfer latency for five hop types (intra-cube horizontal/vertical,
inter-cube horizontal/vertical, inter-SIP) across data sizes 128 B to
10 KB, on both the IPCQ (tl.send/tl.recv) and raw-DMA (tl.load+tl.store)
paths. Emits per-hop PNG plots, an overview PNG, and a CSV summary into
tests/pe2pe_latency_plots/. Latency is reported as max(pe_exec_ns) across
participating PEs, read from engine.get_completion(), so the measurement
captures the SRC/DST PE's kernel body time rather than the full launch+
response-aggregation envelope.

Two simulator fixes were needed to make this measurement meaningful:

- PeMMU now stores a list of (start, end, pa) sub-regions per page
  rather than a single PA. DPPolicy layouts with shards smaller than
  page_size (e.g. 128 B payloads with 4 KB pages) used to silently
  overwrite each other through last-write-wins, causing DMAs intended
  for cube0 to physically route to cube3 - inflating latency by ~170 ns
  per DMA at small sizes. STOPGAP: real MMUs don't support sub-page
  regions; long-term fix is either smaller MMU page size or DPPolicy
  validation that refuses sub-page shards.

- M_CPU's per-PE metrics aggregation (pe_exec_ns, dma_ns, compute_ns)
  now max-merges against the existing value in result_data rather than
  overwriting. Multi-cube workloads share one result_data dict via
  IO_CPU fanout; the previous overwrite caused whichever cube's M_CPU
  finished last to clobber others' values, so multi-cube pe_exec_ns was
  racy and frequently 0. Same fix applied in legacy/builtin/m_cpu.py.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-22 21:04:31 -07:00
ywkang 32536daf2e Fix ADR-0025: IPCQ direction addressing via address-based matching
2-rank bidirectional ring deadlock: when E and W neighbors point to the
same peer, sender-coord matching in _handle_meta_arrival / _credit_worker
picked the first direction in dict order, landing data in the wrong rx
slot relative to what the kernel recv(W) was waiting on.

Fix (ADR-0025 D1/D2/D3):
- install.reverse_direction: prefer OPPOSITE direction (E↔W, N↔S) when
  peer has it pointing back to us; fallback to any matching for
  topologies without opposite convention (tree_binary parent/child).
- _handle_meta_arrival: match by token.dst_addr range against each qp's
  my_rx_base_pa + n_slots × slot_size window (unambiguous).
- _credit_worker: match by credit.dst_rx_base_pa == qp.peer.rx_base_pa.
- IpcqCreditMetadata: new dst_rx_base_pa field carrying receiver-side
  rx base; _delayed_credit_send fills it from the consuming qp.

Tests (Phase 1 → Phase 2):
- test_reverse_direction_opposite_preference_2rank_ring
- test_reverse_direction_opposite_preference_4rank_ring_sanity
- test_meta_arrival_matches_by_dst_addr_same_peer
- test_credit_matches_by_dst_rx_base_pa_same_peer
- Existing credit-return test updated with dst_rx_base_pa.

508 tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-14 00:38:41 -07:00
ywkang 1c8ddc2d03 Fix Phase 1 slot-overwrite race + PE_MATH latency model (n_slots=4 safe)
Root cause: In ring all-reduce, PE_IPCQ's recv handler advances my_tail
and issues a credit return immediately. With tight credit latency
(0.12ns intra-cube), the sender can refill the slot BEFORE the
receiver's outbound PE_DMA reads from it for the next send. The
outbound snapshot then captures stale data from a later round.

Fix: Propagate TensorHandle.data (captured at recv-time, before credit
return) through the entire send chain:
  tl.send(src=handle) → IpcqSendCmd.data → IpcqDmaToken.data
PE_DMA outbound already prefers token.data over MemoryStore read, so
the recv-time snapshot is used for the in-flight data. This eliminates
the race: the snapshot is captured before the slot can be overwritten.

Additional fixes:
- PE_MATH handle_command: compute SIMD latency from output tensor
  element count via _compute_ns(), using max(overhead_ns, compute_ns).
  Previously used overhead_ns=0.0 for all standalone MathCmd, making
  math ops take 0ns in SimPy.
- DataExecutor secondary sort: same-t_start ops sorted by op_kind
  (memory < gemm < math) so IPCQ slot writes execute before math reads.
- ipcq_copy recorded at INBOUND time (receiver PE_DMA arrival) instead
  of outbound. Inbound time is after fabric propagation, so it sorts
  correctly relative to the receiver's math.
- record_copy accepts explicit snapshot parameter (from token.data).

Result: N_ELEM=32 + 256-rank + n_slots=4 + cross-SIP now passes.
n_slots reverted to 4 (the deeper buffer was a workaround, not needed).
502 tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-12 23:02:19 -07:00
ywkang 998cc85762 Add PE-level IPCQ collective infra + unified ccl_allreduce bench (ADR-0023)
Major changes:

PE-level IPCQ infrastructure:
- New PE_IPCQ component: ring-buffer control plane with 4-direction
  neighbor mapping, head/tail pointers, backpressure (poll/sleep).
- PE_DMA extended with vc_comm channel for IPCQ outbound/inbound DMA,
  including in-flight data snapshot (D9) and op_log recording at
  outbound time for Phase 2 replay correctness.
- IpcqDmaToken piggyback model: data + metadata travel together,
  atomic visibility at receiver (invariant I6).
- Credit return fast path: bottleneck-BW latency, no fabric vc_comm.

Phase 2 data execution (ADR-0020 integration):
- op_log extended: DmaWriteCmd now captures src_space/src_addr for
  Phase 2 dma_write copy; ipcq_copy ops recorded at outbound time.
- DataExecutor replays dma_write + ipcq_copy in t_start order.
- Engine._flush_data_phase: incremental cursor-based replay after
  each engine.wait() so host reads see post-Phase-2 data.
- KernelRunner Phase 1 writes disabled when op_log is active to
  prevent stale data from corrupting the MemoryStore snapshot.

TLContext / kernel API:
- tl.send(dir, src=TensorHandle), tl.recv(dir, shape, dtype),
  tl.recv_async, tl.wait(RecvFuture), copy_to_dst mode.
- TensorHandle operator overloading (add/sub/mul/div) via thread-local
  active TLContext → MathCmd dispatch through PE_MATH.
- PE-local scratch allocator for math output handles.
- tl.load returns space="hbm" handles for correct Phase 2 addressing.
- Additional math functions: maximum, minimum, fma, clamp, softmax, cdiv.

Unified ccl_allreduce bench (PyTorch-compat host code):
- Single benches/ccl_allreduce.py with run() + worker(rank, ws, torch)
  split matching real PyTorch DDP worker pattern.
- torch.distributed facade: init_process_group, get_world_size,
  get_rank, get_backend, all_reduce, barrier — only real PyTorch names.
- AhbmCCLBackend: eager install_ipcq at init, all_reduce dispatches
  kernel via tensor shard metadata (n_elem from shards[0].nbytes).
- world_size derived from topology spec (sips × cubes × pes_per_cube)
  with optional algorithm-level override in ccl.yaml.

Tensor API (PyTorch-compat surface):
- Tensor.numpy(): gather-aware (all shards via VA-based addressing).
- Tensor.copy_(source): scatter from host tensor into sharded target.
- RuntimeContext.from_numpy(arr): host-side staging tensor.
- Tensor.data property fixed to use numpy() (was shards[0]-only).

Algorithm modules moved to src/kernbench/ccl/algorithms/:
- ring_allreduce, mesh_allreduce, tree_allreduce, hello_send.
- Each module exports kernel_args(world_size, n_elem) helper.
- ccl.yaml module paths updated to kernbench.ccl.algorithms.*.

Dead code removed:
- 7 per-variant bench files (ccl_allreduce_{tcm,hbm,sram}, etc.).
- _run_ccl_bench greenlet-per-SIP scheduler.
- benches.loader.is_ccl_bench + run_rank detection.
- benches/ccl/ directory.

Tests:
- New test_ccl_allreduce_matrix.py: 7 parametrized cases
  (ring×3 buffers, ring 8/16, mesh 4, tree 7).
- New test_runtime_api_tensor.py: copy_/numpy/from_numpy unit tests.
- Existing tests updated for new import paths + world_size_override.

Docs:
- Korean ccl-author-guide.md and ADR-0023 paths updated.
- New English versions: ccl-author-guide.en.md, ADR-0023.en.md.

502 tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-12 19:36:59 -07:00
ywkang ff2c677a9c Add 2D grid program_id semantics (ADR-0022)
tl.program_id(axis=0) returns local PE id within cube,
tl.program_id(axis=1) returns cube id. Enables cube-aware
sharding in benchmark kernels.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-09 16:49:56 -07:00
ywkang 1d95df4bee Restructure legacy backups, remove pe_accel, fix DMA self-routing
- Move builtin_legacy/ → legacy/builtin/ (cleaner structure)
- Move pe_accel_legacy/ → legacy/pe_accel/
- Remove custom/pe_accel/ (replaced by new builtin)
- Remove pe_scheduler_v2 from components.yaml
- Switch topology.yaml to pe_scheduler_v1 (new builtin)
- Fix PE_DMA self-routing: handle consecutive DMA_READ stages
  (same component consecutive stages processed in-place, not via port)

382 tests passing.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-09 00:02:26 -07:00
ywkang b6eb97c49a Implement ADR-0021: PE pipeline refactor with token self-routing
Step 1-2: Backup existing code
- builtin/ → builtin_legacy/ (unchanged backup)
- custom/pe_accel/ → custom/pe_accel_legacy/ (unchanged backup)

Step 3-4: New pipeline types and tiling
- pe_types.py: StageType, Stage, TilePlan, PipelinePlan, PipelineContext, TileToken
- tiling.py: generate_gemm_plan, generate_math_plan (ported from pe_accel)

Step 5: Component implementations (ADR-0021 D4-D6)
- PE_SCHEDULER: _feed_loop (singleton FIFO feeder) + plan generation
- PE_FETCH_STORE: new component — TCM ↔ Register File
- PE_GEMM: TileToken pipeline + legacy PeInternalTxn dual-mode
- PE_MATH: TileToken pipeline + legacy dual-mode
- PE_DMA: TileToken pipeline + legacy + fabric Transaction triple-mode
- PE_TCM: TcmRequest handler with dual-channel BW serialization

Step 6: Infrastructure
- topology.yaml: pe_fetch_store component + chaining edges
- components.yaml: pe_fetch_store_v1 registration
- builder.py: PE_COMP_OFFSETS, _add_pe_internal_edges, PE view positions
- Tests: node/edge counts, PE component sets updated

All components handle both TileToken (pipeline) and PeInternalTxn (legacy).
Token self-routing: components read next stage from token.plan, chain via out_port.
366 tests passing.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-08 23:35:31 -07:00
ywkang 51004c311c Implement ADR-0020: 2-pass data execution with greenlet kernel runner
Step 1 — Foundation:
- OpRecord/OpLogger: op log infrastructure with t_start stable ordering
- MemoryStore: numpy ndarray tensor-granular storage (reference semantics)
- data_op=True flag on DmaReadCmd, DmaWriteCmd, GemmCmd, MathCmd, CompositeCmd
- numpy/greenlet dependencies added to pyproject.toml

Step 2 — ComponentBase hooks:
- _on_process_start/end hooks in _forward_txn (fabric messages)
- _handle_with_hooks in PeEngineBase (PE-internal commands)
- op_logger optional — zero overhead when disabled

Step 3 — KernelRunner + greenlet:
- KernelRunner: greenlet ↔ SimPy bridge in triton_emu/kernel_runner.py
- TLContext: _emit() method routes to greenlet switch or command list
- tl.load() returns real numpy data in greenlet mode
- Dynamic control flow supported (memory-read based branching)

Step 4 — PE_CPU integration:
- Greenlet mode when ctx.memory_store is set, legacy fallback otherwise
- Refactored into _execute_greenlet/_execute_legacy/_send_response
- ComponentContext gains memory_store and op_logger fields

Step 5 — DataExecutor:
- Phase 2 numpy execution for GEMM/Math ops from op_log
- _compute_math: all unary/binary/reduction ops
- verify(): compare MemoryStore against expected with dtype tolerance

28 new tests, 366 total passing.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-08 00:22:44 -07:00
ywkang eb792e6212 Remove xbar/noc remnants, rule-based cube-view connectors
- Delete xbar.py and noc.py (TwoDMeshNocComponent) — unused since router mesh
- Remove xbar_v1/noc_2d_mesh_v1 from components.yaml
- Fix pe_to_xbar → pe_to_router in routing exclusion set
- Fix xbar_to_hbm_bw_gbs → hbm_to_router_bw_gbs in report.py
- Update all docstrings/comments referencing xbar/bridge → router mesh
- Cube-view connectors: rule-based _connector_points helper
  - PE↔router: single diagonal line (not chevron)
  - UCIe N/S: 45°→horizontal→45°
  - UCIe E/W: 45°→vertical→45°
  - HBM ports: 45°→horizontal→45°

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-06 23:59:12 -07:00
ywkang 5917b3497c Replace xbar/bridge/single-NOC with explicit router mesh (ADR-0019)
- Remove xbar_top/bot, bridge, single noc node from topology
- Each cube_mesh.yaml router becomes a separate SimPy node (r{row}c{col})
- HBM_CTRL consolidated to single node per cube, attached to all routers
- All traffic (DMA data + PE command) routes through same router mesh
- Update AddressResolver (no slice suffix), PathRouter (_adj_local)
- Update ADR-0002~0019, SPEC.md to remove xbar/bridge references
- Regenerate SVG diagrams for new topology structure
- Skip cross-SIP PE_TCM and PE_MMU routing tests (not yet wired)

326 passed, 13 skipped

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-04 17:51:28 -07:00
ywkang 114510d4b9 Add SchedulerV2 (pe_accel), DPPolicy overrides, and new benchmarks
- Add cycle-accurate PE accelerator scheduler (SchedulerV2) with tiled
  GEMM/Math pipelines (DMA_IN → GEMM → MATH → DMA_WB)
- Add DPPolicy num_pes/num_cubes/num_sips overrides for single-PE testing
- Support tuple target_pe for targeting specific PE subsets
- Add gemm_single_pe and gpt3_qkv benchmarks
- Switch default topology to pe_scheduler_v2

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 23:18:49 -07:00
ywkang 63669f82cb Add SIP-level tensor parallelism, component registry YAML, VA offset verification
- DPPolicy: 3-level (sip/cube/pe), unified naming (column_wise/row_wise)
- PE_CPU: auto num_programs from cube shard count
- context.launch(): per-SIP KernelLaunchMsg with local va_base + auto local shape
- deploy_tensor: removed mmus param, MMU mapping is context-only responsibility
- ComponentRegistry: YAML-based lazy loading (components.yaml), impls→builtin rename
- VA offset bench + tests: 2D/1D, standard Triton kernel pattern

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 01:13:17 -07:00
ywkang 08812eda58 Add virtual memory support: PE_MMU, VA allocator, fabric MmuMapMsg
Implement VA/MMU layer (ADR-0011 Phase 1) enabling Triton kernels to use
contiguous virtual addresses on sharded tensors.

Key changes:
- PE_MMU component: hybrid inbox (MmuMapMsg) + sync translate() for PE_DMA
- VirtualAllocator + PEMemAllocator: free-list with coalescing
- MmuMapMsg/MmuUnmapMsg fabric path with SIP-level routing
- DPPolicy-based mapping: replicate=local, sharded=broadcast
- Tensor lifecycle: del + weakref cleanup, context manager
- Rename: TensorHandle.pa→addr, DmaReadCmd.src_pa→src_addr, ctx→torch

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 00:01:47 -07:00
ywkang 62fb01ae18 Add reverse path response latency for PE DMA and PE_CPU→M_CPU
Model fabric response hop latency for PE-internal operations:
- HBM_CTRL sends PeDmaMsg response on reverse path instead of direct done signal
- PE_CPU sends ResponseMsg via NOC→M_CPU on kernel completion
- Add NOC→PE_DMA and PE_CPU→NOC edges in topology builder
- Make HBM BW test assertions dynamic based on topology efficiency

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-20 15:40:56 -07:00
ywkang d75da439c6 Add probe CLI improvements, D2H read, UCIe/HBM tuning, BW sweep
- Probe CLI: restructured output (tables first, routes below), per-hop
  timestamps, split cross-cube into best/worst cases, D2H read section
- UCIe overhead: 1ns -> 8ns per port (16ns per crossing) to fix
  cross-cube-best < cross-half latency inversion
- HBM efficiency: added efficiency=0.8 factor to hbm_ctrl, reducing
  effective BW from 256 to 204.8 GB/s
- Multi-size BW sweep: saturation tables (4KB-1MB) for all probe cases
- Probe default data size: 4KB -> 32KB for more realistic measurements
- IOChiplet NOC + D2H topology and tests
- NOC mesh, xbar, BW occupancy components and tests
- Cube mesh visualization diagram

278 tests pass.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 01:16:18 -07:00
ywkang 6f43807900 commit - release 1 2026-03-18 11:47:48 -07:00