e3f08972da657e8728a7de9b172732ec2dc3c444
§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>
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 GEMMqkv_gemm_multi_pe— multi-PE QKV GEMMipcq_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
- CHANGES.md — changelog with detailed descriptions of each release
- docs/onboarding/latency-model.md — latency model explanation with worked examples
- docs/onboarding/ — onboarding guides (architecture overview, latency model, CCL author guide, intro presentation)
- docs/adr/ — Architecture Decision Records
Description
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