ywkang e8b7f4f064 paper(gemm): per-PE HBM BW + TFLOPS evaluation plots; unify peak to 8 TFLOP/s
Two new evaluation plots for §3 GEMM, both using the composite window
as the denominator (the up-front tl.load of A in load_ref is therefore
excluded — only HBM traffic and compute inside the composite count):

- gemm_hbm_bw_util.png: per-PE HBM bandwidth utilization against the
  256 GB/s per-PE ceiling. Answers "is this workload memory-bound on
  this configuration?" — small/single-tile shapes sit at 22–46%
  (pipeline-fill-limited), large or output-write-dominated shapes
  saturate (≥85%).

- gemm_per_pe_tflops.png: per-PE GEMM throughput against the 8 TFLOP/s
  engine peak. The deep-K K=3072 load_ref case lands at
  7.18 TFLOP/s (~90% of peak) — the configuration's clean win.
  ref_ref at the same shape drops to 3.83 TFLOP/s because the second
  operand doubles HBM pressure and the kernel hits the BW ceiling.
  The variant gap on this chart is the operational cost of NOT
  pre-staging the activation.

Both charts compare two operand-staging variants on every shape:
load_ref ("activation pre-staged", weight-only HBM streaming) and
ref_ref (both A and W streamed from HBM). load_load is omitted —
with both operands pre-staged the composite carries no HBM traffic
and the BW metric collapses to 0.

Analytic / measured peak unified to the single hardware spec
(8.0 TFLOP/s = 8000 flops/ns). T_STAGE is now derived as
tile_flops / peak (= 16.384 ns for the 32×64×32 tile) rather than
the previous hardcoded 16 ns, so MAC efficiency and per-PE TFLOPS%
share the same denominator and never disagree. Side effect: max
analytic-vs-measured gap tightens from 2.2 ppt to 1.4 ppt across
the sweep.

§3 Results restructured: lead with the two new evaluation plots
(BW saturation, achieved throughput), then the MAC-utilization
analytic-vs-measured chart serves as the validation step, then the
per-stage engine wall-clock as the supporting diagnostic. §3
Analysis updated to use the load_ref / ref_ref contrast as the
hardware-software boundary line that motivates the GQA kernel of §5.

§2.4 Accuracy claim updated to match: GEMM analytic-vs-measured
agreement is now "within 1.4 ppt across every swept shape", from
~7.7% at single-tile up to ~90% at K=3072. Subtitle/title vertical
stacking glitch in the matplotlib charts fixed in passing.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-17 10:24:42 -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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