test: add pinned LES baseline and temporary candidate diagnostics

tests/validation/les/gpu_probe.py

@@ -0,0 +1,84 @@
+"""Optional exact-header GPU helper probe. Not run in this audit.
+Run ONLY through the authorized Horsea executor. Uses existing PyCUDA/NumPy.
+This is helper parity, not full collide_dispatch or turbulence validation.
+"""
+
+import argparse, json, pathlib
+from audit import DEFAULT_ROOT, populations, wrapper
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--root", type=pathlib.Path, default=DEFAULT_ROOT)
+    ap.add_argument("--device", type=int, default=0)
+    ap.add_argument("--run-gpu", action="store_true")
+    ap.add_argument("--output", type=pathlib.Path, required=True)
+    args = ap.parse_args()
+    if not args.run_gpu:
+        ap.error("Explicit --run-gpu required; use authorized Horsea executor only")
+    import numpy as np
+    import pycuda.driver as cuda
+    from pycuda.compiler import SourceModule
+
+    cuda.init()
+    ctx = cuda.Device(args.device).make_context()
+    rows = []
+    try:
+        for dim in (2, 3):
+            for cs in (0.16, 0.0):
+                for closed, iters in ((1, 3), (0, 3), (0, 200)):
+                    module = SourceModule(
+                        wrapper(dim, closed, cs, iters, gpu=True),
+                        include_dirs=[
+                            str(args.root.resolve() / "src/CelerisLab/lbm/kernels")
+                        ],
+                        options=["-std=c++11", "--use_fast_math"],
+                        no_extern_c=True,
+                    )
+                    fn = module.get_function("probe")
+                    for case, kind, p, tau in [
+                        ("equilibrium", "equilibrium", 0.0, 0.8),
+                        ("pure_shear", "shear", 0.02, 0.8),
+                        ("isotropic", "isotropic", 0.02, 0.8),
+                        ("cap_active", "shear", 0.2, 0.503),
+                    ]:
+                        f, eq = populations(dim, kind, p)
+                        out = np.zeros(1, np.float32)
+                        fn(
+                            cuda.In(np.asarray(f, np.float32)),
+                            cuda.In(np.asarray(eq, np.float32)),
+                            np.float32(1),
+                            np.float32(1 / tau),
+                            cuda.Out(out),
+                            block=(1, 1, 1),
+                            grid=(1, 1),
+                        )
+                        ctx.synchronize()
+                        rows.append(
+                            dict(
+                                dim=dim,
+                                cs=cs,
+                                closed=closed,
+                                iters=iters,
+                                case=case,
+                                omega=float(out[0]),
+                            )
+                        )
+        args.output.write_text(
+            json.dumps(
+                dict(
+                    device=cuda.Device(args.device).name(),
+                    diagnostic_only=True,
+                    results=rows,
+                ),
+                indent=2,
+            )
+            + "\n"
+        )
+    finally:
+        ctx.pop()
+        ctx.detach()
+
+
+if __name__ == "__main__":
+    main()