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