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The spot finder flagged strong pixels on the device and then labelled them on the host, so every frame sent the packed bitmask back - 2.26 MB on a large detector - and the host walked all of it to recover a few hundred pixels. Do the labelling on the device instead: compact the bitmask into a flat-index-sorted list, find each pixel's backward neighbours by binary search, union them lock-free with path halving, then label, accumulate and filter in one kernel. Only the spot list comes back, and only one stream synchronisation per frame. The gain in the ordinary case is modest - about a quarter off per-image spot finding - because the host algorithm is genuinely fast on a normal frame. What justifies it is the frame that is not ordinary. The host labels a sorted sparse list through a window spanning two detector lines, so its cost is quadratic in how many strong pixels share a line. A lit band of detector rows - a hot module, a panel edge - costs 33 ms at two rows and 377 ms at fifteen, all of it under the pixel cap that was supposed to bound this, and none of it maskable when the cause is a diffraction ring rather than a defect: a ring runs tangent to a row at its top and bottom, which is exactly the shape that hurts. The device version is flat at 0.05 to 0.64 ms across every geometry tried, so an online run no longer stalls a quarter of a second on an ice ring. Rejecting an over-cap frame is now free too, since the count is known before any pixel is written. Also label once and filter three times. The per-image minimum-pixel search runs the extraction at three settings, but that setting only decides which components are kept - it does not change the components - so the search itself need not be repeated. This helps the host path as much as the device one. The resolution mask moves to the device as a bit mask, uploaded when the limits change rather than per frame, since the compaction needs it there. Parity is asserted permanently rather than argued: five cases covering realistic frames, occupancy from a hundred pixels to past the cap, the pathological geometries including rings, the resolution mask, and a hundred-repeat determinism check - requiring the same partition, the same spot order, and identical counts. The centroid is a float sum and therefore order-dependent, so the device walks each component from its root in ascending order and fuses its multiply-add the way the host's does; note that whether the host fuses at all depends on the architecture flags, so exact centroid equality is asserted where the compiler fuses and a two-ulp bound otherwise. Making those accumulators integer would remove that dependence entirely and is worth doing separately. Regression set: all 37 crystals identical to the last printed digit. Unit suite passes with the new cases. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
110 lines
6.1 KiB
C++
110 lines
6.1 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<double> gpu_sum;
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CudaDevicePtr<double> 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<double> host_sum; // clipped raw sum } input to the host threshold computation
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std::vector<double> host_sum2; // clipped raw sum^2 }
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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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