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The expensive part of the sim (the 4-state [Q,I,x,v] discharge ODE) now has a vectorized fixed-step RK4 integrator that runs N configs at once as arrays, with the same saturating-iron physics as the scipy path. One code path runs on numpy (CPU) or cupy (GPU) via a backend shim (gpu/backend.py) -- so it's developed and validated WITHOUT a GPU, then runs on GPU unchanged. Honesty gate (per the project's no-smoke-and-mirrors rule): validated against the scipy solve_ivp reference on 4 configs -- exit velocity, peak current, and dissipated energy all match within ~1-3%, plus a batched energy-conservation check. GPU speed is worthless if the numbers differ, so this test is the point. Benchmark: 5000 configs in 6.8s = 733/s single-threaded numpy, already faster than the 5-core scipy sweep (~430/s); cupy parallelizes the same arithmetic for the real win at large N. Not yet batched: flight-to-trigger, multi-stage chaining, genome decode (so this isn't a drop-in sweep replacement yet -- it's the validated core). Next: batch the full pipeline + run cupy on the server GPU. 89 tests pass. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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