ywkang e3f08972da paper: §2 polish + §5 fresh all-reduce data + kernbench probe table
§2 platform:
- Add a "memory-centric AHBM" sentence to the device-and-execution
  intro: each PE is paired with dedicated HBM bandwidth and on-PE
  TCM, so the performance question is about feeding compute from
  locally-attached memory + moving the unavoidable inter-PE traffic
  efficiently. Makes the AHBM character of the platform visible
  before §2.3 starts unpacking the latency model.
- Promote "Congestion and contention modeling" from \paragraph to
  \subsubsection: this is the platform's core differentiator over a
  peak-BW roofline, so it deserves its own heading.
- Rename "Control-plane (issue) cost model" to "Command dispatch
  overhead model" -- describes what it actually charges (the PE_CPU
  paying a fixed + per-byte cost to push a command to one of the
  accelerator engines) without the more abstract "control-plane"
  framing.
- Add a third Accuracy cross-check from kernbench probe: a
  PE→HBM DMA-distance sweep that confirms monotonic hop progression,
  bandwidth saturation matching the per-edge model, and the built-in
  invariants (D2H >= H2D, cross-CUBE best < worst). New
  Table~\ref{tab:probe-pe-dma} reports per-traversal latency and
  utilisation at 32KiB / 1MiB across five hop classes.
- Captured probe output as figures/probe_pe_dma_summary.txt for
  reproducibility.

§5 PE_IPCQ / all-reduce:
- Re-ran milestone-1h-ccl on current sim_engine (post-ADR-0064 Rev2
  and the IPCQ slot-wrap Phase-2 race fix). Updated the topology-
  comparison table and the buffer-kind caption to the fresh raw
  latencies. Headline ratios are preserved:
    torus vs mesh saving at 96 KB/PE: 24.8% (was 24.8%) -> ~25%
    torus vs ring saving at 96 KB/PE: 19.3% (was 19.3%) -> ~19%
    TCM vs HBM saving at 64 KB/PE: 13.4% (was 13.9%) -> ~13%
    TCM vs SRAM saving at 64 KB/PE: 37.2% (was 38.3%) -> ~37%
  The Executive Summary's "up to ~14%" / "up to ~38%" framing stays
  consistent with these post values.

Artifacts refreshed in src/kernbench/benches/1H_milestone_output/:
- ccl/summary.csv + per-topology PNGs + buffer-kind CSV/PNG
- ccl/comparison_mesh_vs_ring_vs_2DTorus_vs_theoretical_vs_fsim.png
- gemm/* (re-run yields identical pe_window structure; PNGs
  refreshed)

Paper figures synced to the fresh artifacts:
- figures/allreduce_comparison.png
- figures/allreduce_buffer_kind.png

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-16 10:38:47 -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

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