Detector-agnostic performance benchmarking for Key4hep simulations
👉 Open the live dashboard · 📖 Read the docs
k4Bench measures where the time and memory go in DD4hep / Geant4 detector
simulations run through ddsim in the Key4hep stack.
Point it at any DD4hep compact geometry and it will tell you how long a simulation takes, how much memory it needs, and — crucially — which subdetector is responsible. It does this without you editing a single XML file or recompiling anything.
What it does
- ⚡ Geometry sweeps — automatically run a baseline, then re-run with each subdetector removed (or only a chosen subset kept) to measure each detector's cost. The original geometry is never touched.
- ⏱️ Per-event & per-detector timing — C++ Geant4 timing plugins record per-event wall time, RSS memory, and per-subdetector stepping time.
- 📊 Analysis & dashboard — load results into pandas, plot them with the bundled helpers, or browse historical trends across Key4hep releases on the live dashboard.
- 🔭 Detector-agnostic — works on any DD4hep compact XML. FCC-ee detectors (ALLEGRO, IDEA, ILD_FCCee, CLD) and DD4hep's own SiD example are the worked examples and nightly-CI targets, not a limit.
Quick start
The recommended install is from source, so the C++ timing plugins are built and you get the full set of metrics:
# 1. Clone the repository
git clone https://github.com/key4hep/k4Bench.git
cd k4Bench
# 2. Source setup.sh to source Key4hep, make a CVMFS-aware venv, install deps,
# build the timing plugins, and install pre-commit hooks.
source setup.sh
# 3. Install the k4bench command (editable)
pip install --no-build-isolation -e .
# 4. Benchmark a geometry (single particle-gun run)
k4bench --xml $K4GEO/FCCee/ALLEGRO/compact/ALLEGRO_o1_v03/ALLEGRO_o1_v03.xml \
--events 100 \
--ddsim-args="--enableGun --gun.particle e- --gun.distribution uniform"
Want to know each subdetector's cost? Add --sweep:
k4bench --xml ALLEGRO_o1_v03.xml --sweep \
--ddsim-args="--enableGun --gun.particle e- --gun.distribution uniform"
Results print as a summary table and are written as CSV (plus per-event /
per-region JSON) under logs/<geometry>/.
Also on PyPI:
pip install k4bench --no-deps(inside Key4hep) gives you run-level metrics, but not the C++ timing plugins — so per-event and per-detector timing are unavailable. Installing from source is recommended.
Analyse and plot the results
The bundled analysis helpers load a run directory into pandas and produce ready-made Plotly figures:
from k4bench.analysis import load_results, plot_run_overview
df = load_results("logs/ALLEGRO_o1_v03") # one row per run
plot_run_overview("logs/ALLEGRO_o1_v03").show() # bar charts across runs
Documentation
Full documentation — installation, every CLI option, the sweep modes, the timing plugins, the architecture, and the dashboard — lives at:
📖 https://key4hep.github.io/k4Bench/
| I want to… | Start here |
|---|---|
| Install and run my first benchmark | Getting started |
| Understand sweep modes & options | User guide |
| Understand how it works | Architecture |
License
Distributed under the terms of the LICENSE in this repository.
1 maintainer
Metadata
1 maintainer
Release files for k4bench 0.0.43
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Total release size: 1.3 MB
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