# Refined DFG MRT timing harness (measurement only)

`tests/validation/time_refined_dfg.py` times the matched MRT refined/uniform DFG Re100 setup of
`run_refined_dfg.py` (same configuration, geometry and initial state functions). No numerical,
physics-gate or production change; no force or accuracy result is produced.

| Variant | Loop | Force history |
|---|---|---|
| A | refined `step(1)` + `read_raw_exchange` per base step (as the runner) | valid |
| B | refined `step(1)` only | timing-only |
| C | refined single `step(N)` | timing-only |
| E | uniform `sim.run(<=100)` + `read_force` (as the runner) | valid |
| E' | uniform single `sim.run(2N)` | timing-only |

- Equal simulated duration: N refined base steps vs 2N uniform D80 steps.
- Every repeat starts from the same fresh state (refined `initialize`, uniform `snapshot/restore`)
  and the same warm-up; construction/JIT, warm-up, downloads and parity checks are untimed.
- `step()` synchronizes after each base interval and `Simulation.run()` before returning, so the
  wall timer (`time.perf_counter`) brackets completed GPU work. No CUDA events are used.
- Final populations and NaN masks must be bitwise identical to the first A (refined) or E
  (uniform) repeat; any difference aborts.
- Cases run sequentially (uniform, then refined) in one process with startup serialized;
  variants are interleaved within each repeat. A-B and B-C are measured comparative
  differences under GPU variability, not exact causal decompositions.

Usage (root selects one verified idle GPU):

    PYTHONPATH=src python -m unittest tests/unit/test_time_refined_dfg.py
    python tests/validation/time_refined_dfg.py --output <new> --plan
    CUDA_VISIBLE_DEVICES=<gpu> python tests/validation/time_refined_dfg.py --output <new>

Defaults: 2000 base steps, 200 warm-up base steps, 3 repeats. Profiler capture and the
`observe_raw_exchange=False` variant are deferred until these measurements exist.