BeamCenterFFTScore is split the way the FFT indexer is: an engine interface with a cuFFT
implementation and an fftw3f one, chosen by whether CUDA is compiled in, a device is visible
and the card has room for the image. Everything that decides anything - the preparation, the
masked Pearson, the shortlist and the margins - is shared, so the engines can differ only in
how the four convolutions are computed, and the parity test compares two shortlists rather
than two answers.
The whole-detector transform was the entire added cost of the capture (4-15 s a run on CPU,
all of it the transforms). On the device it is milliseconds, so the composition is now
cheaper than the walk it replaced rather than dearer - which is what makes 2x2 binning, the
other way out, unnecessary: full resolution is affordable and the binned capture was measured
to pick a neighbouring peak on one dataset of 51.
Device discipline, because the card is shared with the run's own analysis workers: the four
convolution surfaces are brought back to the host and combined there, so the device holds
only one real buffer and three spectra; the spectrum of the image is reused for its square;
plans and buffers are created inside the call that needs them and freed when it returns; and
an image that would not fit is scored on the CPU instead.
Tests, none of which need a GPU or a dataset: the autoconvolution identity (an exactly
symmetric image is recovered at integer and half-pixel centres, scoring exactly 1), shift
equivariance, the four convolutions against brute force, the no-variance overlap that
VARIANCE_FLOOR exists for, the smooth pad, GPU against CPU, and the composition - a walk
seeded at the capture where the walk alone declines, the fallback to the capture alone at
BEAM_CENTER_CAPTURE_SIGMA_PXL, and the beam stop being blanked out of the scored image.
leonarski_f
6326c8b301