ClaudeandClaude Opus 5.5
6c53d2e74a
Simulation.get_ddf() is flat (9*ny*nx); view it as (Q, cells) before applying the fluid mask, with a flat-DDF regression. Label A/E as runner-like force-read workloads (values discarded, no aggregation or validation; no macro/probe/CSV cost; not benchmark throughput) and split status lines into CONSTRUCTED and WARM_READY. Test/doc only. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
45 lines
3.0 KiB
Markdown
45 lines
3.0 KiB
Markdown
# Refined DFG MRT timing harness (measurement only)
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`tests/validation/time_refined_dfg.py` times the matched MRT refined/uniform DFG Re100 setup of
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`run_refined_dfg.py` (same configuration, geometry and initial state functions). No numerical,
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physics-gate or production change; no force or accuracy result is produced.
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| Variant | Loop | Force reads |
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| A | refined `step(1)` + `read_raw_exchange` per base step | runner-like force-read workload (values discarded) |
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| B | refined `step(1)` only, no host reads (`observe_raw_exchange` stays on) | timing-only |
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| C | refined single `step(N)` | timing-only; ledger keeps the final base interval only |
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| E | uniform `sim.run(<=100)` + `read_force` | runner-like force-read workload (values discarded) |
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| E' | uniform single `sim.run(2N)` | timing-only; at most a whole-window average, different summation order |
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- No variant retains a valid force history; A/E omit the runner's interval aggregation and
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validation, and no variant includes macro/probe/CSV costs. Results are not full benchmark
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throughput.
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- Status lines: `CONSTRUCTED` (construction/JIT complete) and `WARM_READY` (first warm-up and
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health check passed) per case.
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- Equal simulated duration: N refined base steps vs 2N uniform D80 steps.
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- Every repeat starts from the same fresh state (refined `initialize`, uniform `snapshot/restore`)
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and the same warm-up; construction/JIT, warm-up, downloads and parity checks are untimed.
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- The actual stepping stream is synchronized explicitly before and after each timed loop; the
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wall timer (`time.perf_counter`) is the only source. No CUDA-event span is called kernel time.
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- `--max-wall-seconds` is required for GPU runs (outer timeout, checked between variants and
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every 100 steps in per-step loops).
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- Final populations and NaN masks must be bitwise identical to the first A repeat (A/B/C) or the
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first E repeat (E/E'); refined and uniform grids are never compared. Leaf/fluid populations
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must be finite. Physical health (finite leaf/fluid macros, rho in [0.5, 1.5], max Mach <= 0.2,
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the existing runner limits) is checked after warm-up and after each timed window. Matching NaN
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is accepted only as a storage mask; any infinity or level-count mismatch fails parity. All
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parity/health checks are untimed.
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- Cases run sequentially (uniform, then refined) in one process with startup serialized;
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variants are interleaved within each repeat. A-B and B-C are measured comparative
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differences under GPU variability, not exact causal decompositions.
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Usage (root selects one verified idle GPU):
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PYTHONPATH=src python -m unittest tests/unit/test_time_refined_dfg.py
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python tests/validation/time_refined_dfg.py --output <new> --plan
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CUDA_VISIBLE_DEVICES=<gpu> python tests/validation/time_refined_dfg.py --output <new> --max-wall-seconds <s>
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Defaults: 2000 base steps, 200 warm-up base steps, 3 repeats. Profiler capture and the
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`observe_raw_exchange=False` variant are deferred until these measurements exist.
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