mukesh 9e1242039b gqa: single-KV-group LLaMA-3.1-70B prefill milestone (Increments 1-5)
End-to-end wires C=8 P=8 d_head=128 prefill with snake-ring inter-CUBE
SFR, intra-CUBE PE-SP (all 64 ranks active), and the milestone bench
panel. Decode kernel gains lrab-adapted center-root reduce for the
2×4 sub-mesh per ADR-0060 §4.2.

Increment 1 — SFR multi-row snake
  src/kernbench/ccl/sfr_config.py: configure_sfr_intercube_ring gains
  submesh_shape / submesh_origin kwargs; installs a Hamiltonian snake
  ring through a rectangular sub-mesh (every hop is 1-hop physical
  neighbour). Backward-compat: 1D-row behaviour preserved when
  submesh_shape is None.
  tests/test_intercube_snake_ring.py (12 tests)

Increment 2 — Decode lrab-adapted center-root reduce
  src/kernbench/benches/_gqa_attention_decode_long.py: new sub_w param
  (default 0 = existing 1D-chain). sub_w >= 2 selects the ADR-0060
  §4.2 prescribed lrab-adapted Phase 1+2 reduce (bidirectional row +
  bidirectional col converge to the center cube), with log-sum-exp
  _merge_running replacing the plain + of lrab.
  tests/attention/test_gqa_decode_long_2d_reduce.py (4 tests)

Increment 3 — Prefill kernel at C=8 (no production change)
  Verified by inspection that the existing prefill_long kernel +
  Increment 1's snake SFR already work at C=8 without any kernel
  edit. The kernel speaks logical W/E; the snake routes it.
  tests/attention/test_gqa_prefill_long_c8_snake.py (3 tests)

Increment 4 — Intra-CUBE PE-SP in prefill (all 64 ranks)
  src/kernbench/benches/_gqa_attention_prefill_long.py: new P param
  (default 1 = existing PE-0-only). P > 1 splits T_q query-axis-wise
  across the P PEs of each CUBE; output rows are disjoint per PE so
  no intra-CUBE reduce is needed; each PE drives its own same-lane
  ring (P parallel rings).
  tests/attention/test_gqa_prefill_long_pe_sp.py (5 tests)

Increment 5 — LLaMA-scale milestone bench panel
  src/kernbench/benches/milestone_gqa_headline.py: new panel
  single_kv_group_prefill_gqa_c8_p8 (C=8, P=8, T_q=S_kv=32K,
  d_head=128). _run_prefill_panel extended with P/T_q/d_head
  defaults; routes snake SFR when C > mesh_w.
  tests/attention/test_milestone_gqa_single_kv_group_prefill_panel.py (3 tests)

Total: 4 production files modified, 5 new test files, 27 new tests.
Followed the Phase 1/2 protocol per CLAUDE.md throughout.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-15 13:42:31 -07:00
2026-03-18 11:47:48 -07:00
2026-03-18 11:47:48 -07:00

kernbench

A discrete-event simulator for AI accelerator hardware, built on SimPy. It models the full data path — from host PCIe injection through IO chiplet, NOC mesh, crossbar, and HBM — to measure end-to-end latency with contention and queueing.

Architecture

Host (CLI)
  |
  +-- kernbench run     -> run a benchmark (QKV GEMM, AllReduce, ...)
  +-- kernbench probe   -> latency/BW analysis for predefined traffic patterns
  |
  v
+---------------------------------------------------+
|  Runtime API          (runtime_api/)              |
|  MemoryWriteMsg, MemoryReadMsg, PeDmaMsg,         |
|  KernelLaunchMsg                                  |
+---------------------------------------------------+
|  Simulation Engine    (sim_engine/)               |
|  SimPy processes, wire model, BW occupancy        |
+---------------------------------------------------+
|  Components           (components/)               |
|  pcie_ep, io_cpu, m_cpu, noc, xbar, hbm_ctrl,    |
|  pe_cpu, pe_dma, pe_gemm, pe_math, pe_tcm, ...   |
+---------------------------------------------------+
|  Topology             (topology/)                 |
|  YAML-driven graph: 4x4 cube mesh, UCIe links,   |
|  IO chiplet with NOC, HBM slices                  |
+---------------------------------------------------+

Prerequisites

  • Python 3.10+
  • Dependencies: simpy, pyyaml, pytest

Installation

# Create virtual environment
python -m venv .venv

# Activate (Windows)
.venv\Scripts\activate

# Activate (Linux/macOS)
source .venv/bin/activate

# Install in editable mode
pip install -e ".[dev]"

Usage

Probe — Latency and Bandwidth Analysis

The probe command runs predefined traffic patterns (H2D write, D2H read, PE DMA) and reports latency breakdown, bottleneck bandwidth, and utilization.

# Run all probe cases
kernbench probe --topology topology.yaml

# Run a specific case
kernbench probe --topology topology.yaml --case pe-local-hbm

Output includes:

  • Summary tables — actual latency, overhead/drain/wire breakdown, effective BW, utilization
  • BW saturation sweep — utilization at 4KB through 1MB to show saturation threshold
  • Per-hop route traces — cumulative timestamps at every node along the path

Run — Execute a Benchmark

# Run a benchmark on all devices
kernbench run --topology topology.yaml --bench qkv_gemm

# Run on a specific device
kernbench run --topology topology.yaml --bench qkv_gemm --device sip:0

Available benchmarks (in benches/):

  • qkv_gemm — single-PE QKV GEMM
  • qkv_gemm_multi_pe — multi-PE QKV GEMM
  • ipcq_allreduce — IPCQ AllReduce

Tests

# Run all tests (278 tests)
pytest

# Run a specific test file
pytest tests/test_probe.py -v

# Run a single test
pytest tests/test_probe.py::test_h2d_latency_monotonic -v

# Run with output shown
pytest -s tests/test_probe.py

Key test files:

File Coverage
test_probe.py Probe latency invariants, monotonicity, determinism, BW sweep
test_engine.py SimPy engine: submit/wait/complete, routing, multi-SIP
test_bw_occupancy.py Wire BW contention, HOL blocking, back-to-back serialization
test_iochiplet_noc_d2h.py IO chiplet NOC topology, H2D/D2H data paths
test_noc_mesh.py 2D mesh NOC routing, Manhattan distance
test_pe_components.py PE-internal components: cpu, scheduler, dma, gemm
test_routing.py XY routing, address resolution, path finding
test_topology_compile.py YAML topology compilation, node/edge validation

Topology Configuration

The system is configured via topology.yaml. Key parameters:

Parameter Default Description
ns_per_mm 0.01 Wire propagation delay (10 ps/mm)
cube_mesh 4x4 Cube grid dimensions per SIP
ucie.overhead_ns 8.0 UCIe protocol overhead per port (16ns per crossing)
hbm_ctrl.efficiency 0.8 HBM effective BW factor (256 to 204.8 GB/s)
xbar.overhead_ns 2.0 Crossbar arbitration delay
xbar_to_hbm_bw_gbs 256.0 Raw HBM bandwidth per slice

Project Structure

kernbench/
+-- src/kernbench/
|   +-- cli/            # CLI entry points (main, probe, report)
|   +-- common/         # Shared types (Completion, RequestHandle, Trace)
|   +-- components/     # Hardware component models (SimPy processes)
|   +-- di/             # Dependency injection
|   +-- policy/         # Routing (XY), address decoding (PhysAddr)
|   +-- runtime_api/    # Host-facing API (messages, bench runner)
|   +-- sim_engine/     # Discrete-event engine, transaction, wire model
|   +-- topology/       # YAML builder, mesh generator, graph types
|   +-- triton_emu/     # Triton kernel emulation
+-- benches/            # Benchmark implementations
+-- tests/              # pytest test suite (278 tests)
+-- docs/               # ADRs, latency model docs, diagrams
+-- topology.yaml       # System topology configuration
+-- CHANGES.md          # Changelog

Documentation

S
Description
No description provided
Readme 70 MiB
Languages
Python 97.6%
HTML 2.4%