95cecccd8edb360c2c8b5f1feb9b6f76aeddd61a
ADR-0064 Revision 2 review fixes: - D7 NEW: composite size cap (MAX_COMPOSITE_LOGICAL_BYTES default 1024 bytes). Oversized recipes deterministically segmented into N CompositeCmds; each segment incurs its own dispatch cost. Models real HW limits (descriptor queue entry, scheduler parser buffer, command SRAM) and prevents the model from rewarding pathologically-large fused composites. - D2: type-aware extra-field byte counting (int/float=4, bool=1, tuple/list=1+4N, str=1) — replaces uniform 4 bytes per extra. - D3: recalibrated defaults to FIXED=40 cycles, R=0.0625 cycles/byte (16 B/cycle — typical on-die descriptor queue width); anchor stays at ~43 ns for typical 1-OpSpec composite. Clarified anchor description: DMA stages do not appear in logical_bytes (auto-inserted by PE_SCHEDULER from operand.space per ADR-0065 D4). - D4: removed clock_freq_ghz from pe_cost_model: override block; conversion uses the PE node's existing clock_freq_ghz attr. Added max_composite_logical_bytes knob. - Context: emphasized command-count reduction (FIXED) as the primary signal; byte term as secondary refinement. - Open review: added large-composite scheduler-cost stress test. - Test req: added composite-size-cap (#8) and R-sensitivity sweep (#9). ADR-0065 + DDD-0065 follow-on updates: - opt2 vs opt3 dispatch ratio updated 2.4× → ≈4.0× under new defaults (FIXED-dominated, reflecting the corrected framing). - Test req #9: decode opt2 composite fits within 1024-byte cap; no segmentation needed for the GQA workload. - DDD §6: TLContext lowering checks logical_bytes against cap (step 8). - DDD §11: performance model recomputed with new defaults + sensitivity table across R ∈ {0.25, 0.0625, 0.03125} confirming opt2 < opt3 holds. - DDD §9 P6 gate: ratio band 2.4×±10% → 4.0×±15%; sensitivity sweep added. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
gqa(adr): ADR-0064/0065 review fixes — composite size cap, type-aware logical_bytes, R recalibration
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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