getframes 2.5.0

Added

• Arm benchmark data point (benchmarks/device-results-neoverse-n1.*): the
  device table on an Ampere Neoverse-N1 host (16 pinned cores) with an RTX
  4060 and an RTX A400.

Performance

• Small GPU frames run up to 2.6x faster, with bit-identical seeded output.
  WFS-sized frames were launch-bound: the host spent longer issuing about twenty
  small CuPy kernels per frame than the GPU spent running them. The GPU path now
  computes the photo and total expectations in one fused kernel and the whole
  readout (full-well clip, defects, reset/avalanche/read noise, gain, bias
  pedestal and structure, common mode, rounding, ADC saturation, uint32
  conversion) in another; keeps scalar inputs (background, extra_electrons,
  the EM-gain scale, the CIC rate) on the host instead of uploading them every
  frame; samples Poisson counts directly in the working precision; and reuses
  the chain's photo + dark + extra sum as FrameTruth.mean_electrons. The
  noise is drawn by the same calls in the same order and the fused kernels
  repeat the same floating-point operations (FMA contraction off), so every
  seeded GPU frame and truth array is unchanged; a new GPU test compares them
  bit for bit against the separate operations. bench_devices.py on an Ampere
  Neoverse-N1 host (12 pinned cores), frames/s, 2.4.0 → now, median of three
  interleaved runs: RTX 4060 — Pyramid 80x80 2,422 → 6,418 (2.65x),
  Shack-Hartmann 160x160 2,424 → 6,340 (2.62x), OCAM2K 240x240 1,650 → 2,733
  (1.66x), SAPHIRA 256x320 1,809 → 2,072 (1.15x), 1024x1024 240 → 247 (1.03x);
  RTX A400 — Pyramid 2,403 → 6,315 (2.63x), the larger frames 1.02–1.06x.
  Larger frames are bound by CuPy's double-precision Poisson and Gamma
  samplers. See the GPU guide.
• The NumPy path skips whole-frame identity arithmetic (adding a zero offset,
  dividing by a unit gain, multiplying by a unit avalanche-gain map) and reuses
  the expectation sum as the truth: 1–5% faster (1024x1024: 13.4 → 14.1 frames/s
  on the same host), with identical seeded output. It remains bound by NumPy's
  Poisson sampler (about 80% of a 1024x1024 frame), whose single sequential
  stream cannot be threaded without changing seeded frames.

Fixed

• benchmarks/bench_devices.py reported the CPU of Arm hosts as aarch64;
  it now reads the model from lscpu (e.g. Neoverse-N1) when
  /proc/cpuinfo has no model name.