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Jungfraujoch/image_analysis/spot_finding/AdaptiveSpotFinderGPU.h
T
leonarski_fandClaude Opus 5 abb94ca450 spot_finding: accumulate the adaptive ring statistics in integers
The per-ring sums were floats reduced by atomics, so the ring sigma - and with it the
detection threshold - depended on the order the blocks happened to arrive in. Detection
compares an INTEGER pixel value against that threshold, so a threshold that drifts
across an integer flips every pixel of that value in the ring at once, which is how a
last-bit difference turned into a different spot list.

A preprocessed pixel is an exact int32 and the masked and saturated sentinels are
skipped, so v and v*v are exact in 64 bits, and integer addition is associative: the
sums no longer care about arrival order. Both engines now accumulate the same way, so
they agree exactly rather than approximately, and the GPU spot list is bit-identical
across runs. The corrected sums that feed the reported azimuthal profile stay float -
a pixel value times a float correction has no exact integer form - but they do not
enter the detection decision.

Cost: the ring reduction needs 28 bytes per bin instead of 20 in the plain pass, which
drops it from eight co-resident blocks per SM to seven and costs about 11% of that
kernel (0.582 -> 0.650 ms/frame on a 4.5 Mpx frame). End to end it does not show:
alternating runs on three rotation crystals came out the same or slightly faster, and
the battery is unchanged in every number. The CPU engine got 30% faster (32.2 -> 22.6
ms/frame), integers being cheaper than doubles.

Tests: exact CPU/GPU agreement on the spot list, and 50 repeats of bit-identical output
where there were four.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-03 15:19:11 +02:00

110 lines
6.2 KiB
C++

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#pragma once
// GPU adaptive spot finder that FUSES azimuthal integration and spot finding into one image pass.
//
// The CPU adaptive finder (AdaptiveSpotFinderCPU) and the azimuthal integrator both bin every pixel
// into resolution rings and reduce (sum / sum^2 / count). Today azint runs on the GPU while the
// adaptive finder re-does the identical per-ring reduction on the HOST - a wasted second pass over a
// ~10 MP image. This engine does the ring reduction on the GPU and drives BOTH products from it:
// - the azimuthal-integration profile (mean intensity per ring, in flat-field-corrected space), and
// - the per-ring background (mean, sigma, peak-excluded via two sigma-clip passes) that sets the
// self-calibrating spot-detection threshold (in raw photon counts).
// It then flags strong pixels (value >= ring threshold) into a packed bit buffer and hands that
// buffer - still on the device - to SpotExtractorGPU, which builds the spots there.
//
// Numerically it reproduces AdaptiveSpotFinderCPU: the same three-pass robust background, the same
// per-ring threshold formula (shared via AdaptiveThreshold.h, computed on the host once per frame),
// and the same raw-count detection test. The only differences from the CPU are those inherent to a
// GPU reduction (float per-ring accumulation in atomic order vs the CPU's serial double sums), which
// shift a handful of borderline pixels at most. The corrected sums for the azint profile are
// accumulated in the SAME plain first pass, so one reduction feeds both products.
#include <memory>
#include <vector>
#include "ImageSpotFinder.h"
#include "SpotExtractorGPU.h"
#include "SpotFindingSettings.h"
#include "../../common/AzimuthalIntegrationProfile.h"
#include "../../common/AzimuthalIntegrationMapping.h"
#include "../indexing/CUDAMemHelpers.h"
#include "../indexing/CudaSharedTables.h"
class AdaptiveSpotFinderGPU : public ImageSpotFinder {
const AzimuthalIntegrationMapping &mapping;
std::shared_ptr<CudaStream> stream;
const int nbins;
const size_t npix;
int reduce_threads = 128;
int reduce_blocks = 0;
int flag_threads = 256;
int flag_blocks = 0;
size_t shared_plain = 0; // per-block shared bytes for the plain pass (raw + corrected rings)
size_t shared_clip = 0; // per-block shared bytes for a sigma-clip pass (raw rings only)
bool use_shared = true; // false -> nbins too large for shared memory, use the global-atomics kernel
// Static mapping inputs: geometry-only, so one copy per GPU shared with every other engine on it
// (see CudaSharedTables.h) rather than one copy per worker thread.
std::shared_ptr<CudaDevicePtr<uint16_t>> gpu_pixel_to_bin;
std::shared_ptr<CudaDevicePtr<float>> gpu_corrections;
// Raw per-ring accumulators (re-zeroed each pass) + derived stats used to clip and threshold.
// double, like the CPU engine's ring accumulators: the ring sigma is the cancelling difference
// sum2/n - m^2, and the block atomics that fill these arrive in an arbitrary order.
CudaDevicePtr<unsigned long long> gpu_sum;
CudaDevicePtr<unsigned long long> gpu_sum2;
CudaDevicePtr<uint32_t> gpu_count;
CudaDevicePtr<float> gpu_mean; // per-ring raw mean (clip predicate)
CudaDevicePtr<float> gpu_sigma; // per-ring raw sigma (clip predicate)
// Corrected per-ring accumulators (plain first pass only) -> azimuthal-integration profile.
CudaDevicePtr<float> gpu_sum_corr;
CudaDevicePtr<float> gpu_sum2_corr;
// Per-ring detection threshold (host-computed, uploaded) and the strong-pixel bit buffer.
CudaDevicePtr<float> gpu_thr;
CudaDevicePtr<uint32_t> gpu_strong;
// Host mirrors of the small per-ring transfers.
std::vector<unsigned long long> host_sum; // clipped raw sum } input to the host threshold computation
std::vector<unsigned long long> host_sum2; // clipped raw sum^2 } (exact integers - see the kernel)
std::vector<uint32_t> host_count; // clipped raw count }
std::vector<float> host_thr; // per-ring threshold (empty -> frame had no valid pixels)
std::vector<float> prof_sum; // plain corrected sum } azimuthal-integration profile
std::vector<float> prof_sum2; // plain corrected sum^2 }
std::vector<uint32_t> prof_count; // plain pixel count }
SpotExtractorGPU extractor; // builds the spots from gpu_strong without it leaving the device
AzimuthalIntegrationProfile last_profile; // filled every Run(), retrievable via GetProfile()
// One reduction pass over the image into the raw accumulators. clip_k <= 0 -> plain pass (all
// valid pixels); clip_k > 0 -> keep only pixels within clip_k sigma of the current gpu_mean.
// accumulate_corrected additionally fills gpu_sum_corr/gpu_sum2_corr for the profile (plain pass).
void ReducePass(const ImagePreprocessorBuffer &image, float clip_k, bool accumulate_corrected);
// Finalize gpu_mean/gpu_sigma from the current raw accumulators (per ring).
void FinalizeStats();
// Host: per-ring threshold from the clipped raw stats and the single knob E (false pixels/frame).
void ComputeThresholds(const SpotFindingSettings &settings);
public:
AdaptiveSpotFinderGPU(const AzimuthalIntegrationMapping &mapping, std::shared_ptr<CudaStream> stream);
~AdaptiveSpotFinderGPU() override = default;
AdaptiveSpotFinderGPU(const AdaptiveSpotFinderGPU &) = delete;
AdaptiveSpotFinderGPU &operator=(const AdaptiveSpotFinderGPU &) = delete;
void Detect(const ImagePreprocessorBuffer &image, const SpotFindingSettings &settings) override;
void SetResolutionMask(const std::vector<bool> &mask) override;
const std::vector<DiffractionSpot> &ExtractComponents(const ImagePreprocessorBuffer &image,
const SpotFindingSettings &settings) override;
// The azimuthal profile computed as a byproduct of the last Detect() - lets this engine replace the
// separate azint pass in the analysis pipeline.
[[nodiscard]] const AzimuthalIntegrationProfile &GetProfile() const { return last_profile; }
};