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Jungfraujoch/image_analysis/bragg_integration/BraggIntegrationEngineGPU.h
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leonarski_fandClaude Opus 5 61d24db59f Bragg integration: elongate the background ring per reflection
The signal disk and the r2..r3 background ring were fixed pixel circles, identical for every
reflection at every resolution. A reflection is not round: a finite bandwidth streaks it radially by
bw_sigma*Rpx, so at high resolution the ring sits within 1.3-2.2 sigma of the reflection's own
profile and measures its tails as background.

--integration-stencil <k> makes the RING an ellipse, elongated along the beam->reflection direction
by k times that streak, capped at 2*r3. The tangential half-widths stay r2 and r3, and the r1 signal
disk stays a circle: r1 drives the all-or-nothing n_inner_valid == n_inner gate, so growing it
rejects any reflection carrying one bad pixel along a long streak, and the flux a circular r1 loses
is a function of resolution alone, which the per-shell scale absorbs.

The geometry lives in one shared header compiled by both the host compiler and nvcc, so the seven
pixel-classification sites - the CPU mask/main/clip loops and the GPU mark_mask/main/trim/clip
kernels - cannot drift apart. Rather than evaluate an ellipse, each pixel's squared distance has its
radial part scaled down, d2 - q*rad^2 against r2^2/r3^2 with q = 1 - (r/(r+grow))^2, so grow = 0
gives q = 0 and both tests collapse onto d2 exactly in floating point.

The width is the bandwidth streak alone, not the profile's full radial variance, which also carries
the sensor parallax and weak-spot capture terms. Deriving the growth from those was implemented
first and measured on the rotation battery: at k=1 it took Thau_9's high-shell CC1/2 from 75.8 to
27.9 and Benas_3's from 14.1 to 6.0, against cytC_10 +1.2 and lyso_ref flat. On a monochromatic beam
they are the only terms there are, and C_CAPTURE is 64% of them. Keeping only the streak also makes
the option exactly inert without a bandwidth, rather than merely small.

Default 0. Measured on broadband rotation data with the bandwidth set to its spectroscopic value,
matched resolution limits: high-shell CC1/2 30.6 -> 46.4 at k=4, and better in EVERY shell in both
CC1/2 and R_meas (top shell R_meas 194.7% -> 138.7%), with completeness, multiplicity and space
group unchanged and 28 of 98833 unique reflections lost. Anomalous peak height over 18 sites
+0.107 +- 0.039 sigma (p = 0.013). The full 38-crystal rotation battery is unchanged to every
reported digit, base against k=3.

Two consequences of an elongated ring are handled rather than inherited. The neighbour exclusion
marks the inner ELLIPSE in each neighbour's own frame, or an elongated neighbour leaks its tails
into this reflection's ring. And the radial-background curvature kernel becomes a small table
indexed by the growth, because its azimuthal average makes one kernel serve every reflection only
while their stencils are identical; the GPU's radial window, previously a fixed 32 bins, is now
sized on the host from the widest ring on the detector.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-10 15:19:28 +02:00

68 lines
3.5 KiB
C++

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#pragma once
#include <cstdint>
#include <memory>
#include <vector>
#include "BraggIntegrationEngine.h"
#include "../indexing/CUDAMemHelpers.h"
// CUDA engine: reproduces BraggIntegrationEngineCPU up to floating-point precision. Each stage is a
// kernel with one CUDA block per reflection cooperating over the small window via shared-memory
// reductions (the natural mapping for thousands of independent, tiny per-spot integrations).
//
// Pipeline (profile modes): reset -> mark_mask -> boxsum -> learn_profile -> build_profiles -> fit
// (the resolution shell is computed inline, so there is no separate shell pass). BoxSum mode stops
// after boxsum (that pass is the BraggIntegrate2D box integrator and the seed of the profile fit).
// The preprocessed image already lives on the device (ImagePreprocessorBufferGPU::getGPUBuffer());
// only the per-frame predicted centres are uploaded.
class BraggIntegrationEngineGPU : public BraggIntegrationEngine {
std::shared_ptr<CudaStream> stream;
int threads;
size_t fit_shared_bytes;
int rad_w = 0; // radial-background window of boxsum, in bins of one pixel
size_t boxsum_shared_bytes = 0;
size_t capacity = 0; // per-reflection device/host arrays hold at least this many reflections
// --- per-reflection device arrays (grown by EnsureCapacity) ---
CudaDevicePtr<float> d_px_x, d_px_y, d_d;
CudaDevicePtr<int> d_cx, d_cy;
CudaDevicePtr<float> d_I, d_sigma, d_bkg, d_bkg_var, d_var_bkg, d_obs_x, d_obs_y;
CudaDevicePtr<float> d_isum; // box-sum raw sum, for the radial correction
CudaDevicePtr<int> d_ninner, d_rbin, d_kbin;
CudaDevicePtr<uint8_t> d_ok, d_strong, d_has_obs;
// --- radial background curvature correction (see BraggIntegrationEngine) ---
int n_rad = 0; // radial bins, 0 when the correction is off
CudaDevicePtr<float> d_rad_sum, d_k_diff;
CudaDevicePtr<int> d_rad_cnt;
// --- fixed-size device arrays ---
// The learning/fit math is single precision: FP64 is heavily throttled on consumer GPUs and the
// extraction is Poisson-noise limited, so float reproduces the double CPU path to ~1e-4.
CudaDevicePtr<uint8_t> d_mask; // per-pixel inner-stencil reflection mask
CudaDevicePtr<float> d_shell_grid, d_global_grid; // learned profile accumulators (N_SHELL*GG, GG)
CudaDevicePtr<float> d_shell_P, d_global_P; // normalised profiles (empirical mode)
CudaDevicePtr<float> d_mom; // learned 2nd moments, 3 per shell + global
CudaDevicePtr<float> d_sigma2_r, d_sigma2_t; // radial/tangential widths, N_SHELL + global
CudaDevicePtr<int> d_shell_n, d_global_n;
CudaDevicePtr<unsigned long long> d_invd2; // [min,max] inv-d^2 as monotonic bit patterns
// --- host staging (copied back once per frame) ---
std::vector<float> h_px_x, h_px_y, h_d;
std::vector<float> h_I, h_sigma, h_bkg, h_var_bkg, h_obs_x, h_obs_y;
std::vector<uint8_t> h_ok, h_has_obs;
void EnsureCapacity(size_t n);
public:
BraggIntegrationEngineGPU(const DiffractionExperiment &experiment, std::shared_ptr<CudaStream> stream);
std::vector<Reflection> Run(const ImagePreprocessorBuffer &image,
const std::vector<Reflection> &predicted, size_t npredicted,
int64_t image_number) override;
};