2d8271c9818a898a08a99f2cd87487ec19f4bf12
ADR-0064 D8 broadened from "DMA fast-path" to "single-op-cmd fast-path": every single-op command (DmaRead/DmaWrite/Gemm/Math/Copy) now pays the lighter FIXED=8; only CompositeCmd keeps the 40-cycle control-path FIXED (it alone needs scheduler plan generation + per-tile RW-hazard tracking + completion wiring). Renamed knob fixed_per_dma_cmd_cycles -> fixed_per_single_op_cmd_cycles. dispatch_cycles now branches on "is CompositeCmd" rather than enumerating DMA types. Term choice: "single-op" (not "atomic", which read as sync/async) — the axis is composition (one engine op vs fused multi-op plan), orthogonal to timing. single-op <-> composite. Tests: test_pe_cost_model.py updated to the single-op surface (defaults, fast-path over all 5 single-op cmd types, composite general path, yaml override). All green. Recalibrated tests/attention/test_gqa_decode_opt2.py ::test_opt3_dispatch_exceeds_opt2 — NOT a regression: D8 makes single-op cmds 5x cheaper, so opt2's two-composite fusion win over opt3's many single-ops narrowed from pre-D8 ~3.7x to ~1.87x (opt3=224 > opt2=120). The CPU-offload invariant (opt2 cheaper) still holds; only the model- dependent ">2x" constant was over-fit to the old uniform-40 model. Gate now: direction + >1.5x margin (matches sibling R-sweep test's stated "absolute ratio informative-only" philosophy). NOTE for review: ADR-0065's "2x CPU-offload win" headline may want a refresh to reflect the post-D8 ~1.87x — left to user (architectural doc). Full regression: 826 passed, 1 skipped (tests/ excl. tests/gemm). --- Remaining work (resume here if interrupted) --- 5. Re-run scripts/paper/paper_plot_gemm_async_vs_composite.py with new cost model; verify async-tiled dispatch overhead drops (~4576ns -> ~1536ns expected) and the composite-vs-async-tiled gap narrows from the prior ~6.3x at K=3072. 6. Copy regenerated gemm_composite_vs_async_tflops.png to docs/report/1H-codesign-paper/figures/. 7. Paper §3.4 (03-gemm.tex sec:gemm-vs-async): finish naive->async-full / chunked->async-tiled rename AND reframe FIXED_DMA wording to single-op vs composite (currently still says "lighter FIXED for DMA descriptors, FIXED_DMA=8"). Table 2 (02-platform) + §2 dispatch prose already done. 8. Paper §3.4 K=3072 corner para + mechanism #3: update dispatch breakdown to new model (96 DMA*8 + 95 single-op*8 ≈ 1.5us vs old 4.6us); update headline ratio if it changed. 9. Rebuild docs/report/1H-codesign-paper/build/main.pdf (tectonic) + verify via pdftotext. 10. Then this is the bench-harness + paper commits (Groups 2 & 3). Co-Authored-By: Claude Opus 4.8 (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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