Installation and Quick Start¶
Requirements¶
- Python 3.10 or newer
- No runtime Python dependencies
pytest8 or newer only when running tests
Install in editable mode¶
From the Cnuas repository root:
Without installing, place the package on PYTHONPATH:
The remaining examples assume:
Predict one component¶
Estimate CnuasNIC service for four packets carrying 4 KiB:
The result is JSON:
{
"component": "cnuasnic",
"duration_ns": 427.68,
"evidence": "analytic",
"target": null,
"uncertainty_pct": null
}
The analytic evidence label and null target are important. This is the result
of the selected profile, not measured NIC latency.
Simulate an end-to-end pipeline¶
The result includes:
- profile name and version;
- evidence classes used by the pipeline;
- job count and elapsed model time;
- mean and nearest-rank p95 latency;
- completed jobs per model second.
Add the full event trace:
cnuas-timing simulate profiles/cnuas-analytic-v0.json \
examples/rdma-gpu-pipeline.json --jobs 4 --trace > run.json
Each event identifies its job, stage, component, lane, stage arrival, service start, and completion in picoseconds.
Model arrival rate and queueing¶
Submit a job every 500 ns:
cnuas-timing simulate profiles/cnuas-analytic-v0.json \
examples/rdma-gpu-pipeline.json \
--jobs 1000 --arrival-interval-ns 500 --trace > queued.json
Compare each event's submitted_ps and start_ps. Their difference is the
queue time at that stage. A growing difference identifies a resource that
cannot keep up with the configured arrival rate.
Generate QEMU virtual-time arguments¶
Output:
This is a separate TCG experiment mode. Do not append it to the standard KVM launch command. The command prints a deterministic guest-clock configuration; it does not start QEMU or capture counts.
Run the checks¶
The suite contains 46 checks covering arithmetic, invalid inputs, queueing, parallel lanes, replay, profile I/O, QEMU mapping, fitting, validation, and CLI workflows.
Next steps¶
- Modify workloads and stages in Profiles and pipelines.
- Add hardware observations with Calibration and validation.
- Read Limits and interpretation before reporting results.