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Jungfraujoch/image_analysis/spot_finding/AdaptiveSpotFinderGPU.h
T
jungfrauandClaude Opus 5 5ee0f22a61 Build the detector's lookup tables once, not once per worker
The image loop gives every worker its own analysis engine, so a run builds ninety-six of them. Each
one derived, from scratch, tables that are the same in all of them: the byte-per-pixel mask, the
resolution mask, the radial kernel, and the checksum that names the shared device tables.

The checksum was the worst of it, because it is part of the cache KEY and so is computed before the
lookup - a hit still hashed the whole table. On a 16 Mpx detector that is the bin table, the
corrections and the mask, 126 MB an engine, about twelve gigabytes over a run, to answer a question
whose answer had not changed. The header said it cost nothing measurable; a profile says otherwise,
and says it is worst exactly during the ramp when the machine has nothing else to do.

It cannot simply be remembered against the address, which is what it exists to catch: a buffer can
be freed and another allocated where it was, and the cache would then hand back a device copy of
something else. So the owner of the bytes computes it instead. The azimuthal mapping writes its two
tables in its constructor and never again. The pixel mask re-derives its binary form and its
checksum on every path that changes the mask, and all of those paths are now private to the class.
The key therefore still describes the bytes as they are at the moment of the lookup.

The resolution mask was two passes over every pixel - a float comparison into a vector<bool>, then a
bit-by-bit repack - in each of the ninety-six. It is one pass now, writing the packed form directly,
built once for the limits asked for and handed out as a shared pointer so a worker keeps the mask it
was given. The radial kernel is cached on the six numbers it is derived from.

Nothing computes a different value; only who computes it changes. Byte-identical merged output on a
16 Mpx set and on a small one.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011n8riB6X59oRjkrSHzNPAU
2026-08-23 12:59:58 -04:00

126 lines
7.3 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 = 256;
int reduce_blocks = 0; // global-atomics fallback
int reduce_blocks_plain = 0; // as many blocks as actually fit, per shared-memory footprint
int reduce_blocks_clip = 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> host_bkg; // clipped per-ring mean, NaN where the ring is too sparse to trust
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 }
// Every per-ring array above is a device-to-host copy once per frame. A D2H copy into PAGEABLE
// memory blocks the host until it completes, whatever stream it was issued on - which would stall
// Detect() between the plain pass and the clip passes, with the device then idle while the host
// enqueues them. Pinning the destinations makes the copies genuinely asynchronous, as the
// azimuthal-integration engine already does with its own.
CudaRegisteredVector<unsigned long long> host_sum_reg;
CudaRegisteredVector<unsigned long long> host_sum2_reg;
CudaRegisteredVector<uint32_t> host_count_reg;
CudaRegisteredVector<float> prof_sum_reg;
CudaRegisteredVector<float> prof_sum2_reg;
CudaRegisteredVector<uint32_t> prof_count_reg;
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 SetResolutionMaskBits(const std::vector<uint32_t> &packed_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; }
[[nodiscard]] const std::vector<float> &GetRingBackground() const override { return host_bkg; }
};