This release is a pre-release and may not be stable for production use.
monoprop-bench-tools
The benchmark harness behind monoprop:
the pieces that are reusable outside a single benchmark run, packaged separately
so scripts, notebooks and third-party comparisons can depend on them without
vendoring the repository's benches/ directory.
| Module | What it gives you |
|---|---|
monoprop_bench_tools.memory.cpu |
Exact peak-RSS measurement from the kernel's VmHWM. |
monoprop_bench_tools.memory.gpu |
The device-side counterpart, over CUDA memory-pool counters or a CuPy allocation hook. |
monoprop_bench_tools.models |
Builders for the benchmarked problems: a configurable random problem, a 120-mode Fermi-Hubbard model, and a 127-qubit kicked-Ising model. |
monoprop_bench_tools.report |
Renders a run's artifacts into a side-by-side Markdown report. |
monoprop_bench_tools.bmf |
Renders a run's artifacts into Bencher Metric Format JSON. |
Install
pip install monoprop-bench-tools
GPU support
monoprop_bench_tools.memory.gpu needs CuPy, and only
CuPy. Pick the extra matching your CUDA toolkit:
pip install "monoprop-bench-tools[gpu-cuda12]" # CUDA 12.x
pip install "monoprop-bench-tools[gpu-cuda13]" # CUDA 13.x
Pick one, not both: the two CuPy builds ship the same top-level cupy package.
On a host with no system CUDA toolkit, add CuPy's bundled one with
pip install "cupy-cuda12x[ctk]".
Without CuPy the module still imports, and every reading reports
Method.UNAVAILABLE instead of a figure you might mistake for a real one.
Use
Measure the peak resident footprint of a block, exactly (no sampling):
from monoprop_bench_tools.memory.cpu import HighWaterMark
from monoprop_bench_tools.models import build_hubbard_problem
with HighWaterMark() as hwm:
propagator, circuit = build_hubbard_problem()
propagator.build_graph(circuit)
print(f"{hwm.peak_mb:.0f} MB peak, exact={hwm.exact}")
Render the artifacts of a run:
monoprop-bench-report benches/results # writes REPORT.md
monoprop-bench-bmf benches/results ci-linux # BMF JSON on stdout
Both read the two files a run leaves in the results directory:
time-<label>.json (pytest-benchmark timings) and <label>.json (memory,
operator sizes, configuration). The schema is documented in the repository's
benches/results/README.md, and written by the suite in benches/conftest.py.
Scope
This package deliberately holds no benchmarks. monoprop's own suite lives in the
repository's benches/ directory, because its benchmark names are the key
Bencher's history is stored under and must not move with a library release.
License
Apache-2.0. See LICENSE.
1 maintainer
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