coli: measured Windows launcher defaults — OMP tuning parity + DIRECT/PIPE/PILOT_REAL
On Windows the engine self-exec OMP tuning never runs (Linux/FreeBSD-only)
and posix_fadvise readahead is a compat.h no-op, so a stock Windows run
leaves large measured wins on the table. The launcher now setdefaults, on
win32 only, each independently overridable by setting the variable:
- OMP_WAIT_POLICY=active, GOMP_SPINCOUNT=200000, OMP_DYNAMIC=FALSE,
OMP_NUM_THREADS=<physical cores> (parity with the glm.c self-exec block;
COLI_NO_OMP_TUNE disables exactly this block, presence-based like the
engine). OMP_PROC_BIND/OMP_PLACES deliberately omitted and also removed
from environment_for_plan on win32: MinGW libgomp has no affinity support
("Affinity not supported on this configuration").
- DIRECT=1: unbuffered expert reads. Measured on a 9950X3D + Samsung 9100
PRO Gen5 + Win11: iobench 10.68 GB/s O_DIRECT vs 9.03 buffered (warm);
end-to-end REPLAY 0.48 -> 1.02 tok/s. Matches #162 (1.47x same class).
- PIPE=1: load/matmul overlap, byte-identical output; +8% on top of DIRECT
(PIPE_WORKERS untouched at 8 - 4/8/16 swept flat on Gen5).
- PILOT_REAL=1: real cross-layer prefetch, the only working prefetch on
Windows; +11% and expert hit rate +19 points.
Full ladder methodology and numbers: 96-token greedy REPLAY, one lever per
step, medians of 3-4 runs (see the fork tuning doc referenced in the PR).
tests/test_env_defaults.py covers the defaults, explicit-override-wins,
the kill-switch scope, and the non-win32 no-op.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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@@ -112,8 +112,13 @@ class ResourcePlanTest(unittest.TestCase):
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self.assertEqual(env["COLI_CUDA"], "1")
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self.assertEqual(env["COLI_GPUS"], "1")
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self.assertEqual(env["OMP_NUM_THREADS"], str(plan["cpu"]["physical_cores"]))
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self.assertEqual(env["OMP_PROC_BIND"], "spread")
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self.assertEqual(env["OMP_PLACES"], "cores")
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if sys.platform == "win32":
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# MinGW libgomp: niente affinity su Windows, le chiavi non vanno emesse
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self.assertNotIn("OMP_PROC_BIND", env)
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self.assertNotIn("OMP_PLACES", env)
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else:
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self.assertEqual(env["OMP_PROC_BIND"], "spread")
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self.assertEqual(env["OMP_PLACES"], "cores")
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self.assertEqual(env["PIN_GB"], env["CUDA_EXPERT_GB"])
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explicit_threads = environment_for_plan(plan, {"OMP_NUM_THREADS": "7",
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