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**Files written by Jungfraujoch now import correctly in DIALS, XDS and pyFAI.** A tilted detector, a grid scan, a still recorded at a goniometer position, and saturated or unreadable pixels were each described in a way that a third-party program acted on wrongly. If you process Jungfraujoch data outside Jungfraujoch, prefer this release to any earlier one. * HDF5: the detector tilt (`rot1`/`rot2`/`rot3`) is exported correctly in the NXmx transformation chain; untilted geometries are unaffected. * HDF5: a still recorded at a goniometer position is no longer read back as a single image, and a grid scan records a stationary spindle so a program that requires a rotation axis can open it. * HDF5: the sample transformation chain is written in mounting order, with a Smargon head position told apart from the spindle, one entry per image, `module_offset` as a float unit vector, and `offset_units` on every offset. * HDF5: saturated, underloaded and unreadable pixels are described so a downstream program masks them - `saturation_value`, `underload_value`, `error_value` and `bit_depth_readout` are written correctly, and a data file missing next to a VDS master reads as the error marker rather than as zero counts. * HDF5: the rotation axis is read back under whatever name it carries, and `mirror_y` records whether the assembled image is mirrored in Y relative to the detector's raw readout. * A grid scan and a goniometer axis can both be set; they are no longer alternatives. * `images_per_file` is chosen from the acquisition when it is not given: a rotation sweep of at most 20000 images goes into a single data file, a grid scan splits on whole fast-axis rows, and stills and serial keep 1000. * The writer refuses a stream whose start message declares a different pixel format than its images carry, and a DECTRIS detector sending signed images is no longer declared unsigned. * The image stream can carry the sample transformation chain (`transformations`, in the END message); a producer that does not send it gets the same chain built by the writer. * rugnux: fixing the space group with `-S` no longer prevents the lattice from being found - a lattice indexed in a different setting is reindexed into that group's own setting, and a run whose crystal does not have that group's lattice stops and names the cell it indexed as, rather than reporting statistics that cannot describe it. * rugnux: the per-image resolution estimate now predicts the resolution the merged data reach rather than the highest-resolution spot found, and is reported as `SPOT_RESOLUTION_ESTIMATE`. * rugnux: two runs of the same command on the same images produce the same merged intensities; the azimuthal profile written alongside them is not yet reproducible in the same way. * rugnux: the offline lattice refinement is bounded by iterations rather than by a wall clock, so a loaded machine can no longer refine to a different lattice; a live acquisition keeps its real-time bound. * rugnux: the detector-frame modulation correction is fitted on a grid spanning the detector, so whether it is applied no longer depends on how far integration reached. * rugnux: the geometry pre-pass no longer writes `<prefix>_01.mtz`, `_01.cif`, `_01.hkl` and `_01_image.dat`; the refined second pass writes those files under `<prefix>`, and that is the result to use. * rugnux: `_process.h5` describes the pixel format of the images it links to, and is written on a thread of its own. * rugnux: the detector geometry is also logged in XDS's convention (`ORGX`/`ORGY`, detector axis vectors, rotation axis), so it can be compared with an XDS refinement. * rugnux: an image integrated in pyFAI through the `.poni` file written by `--mode calibration` comes out with the correct azimuth, and the file declares pyFAI's `orientation`, which needs pyFAI 2024.01 or newer. Radial integration is unchanged. * rugnux: a rotation run is substantially faster throughout - beam-stop detection, first-pass indexing, geometry refinement, integration, scaling and merging - and observations outside the scaling resolution range are dropped as they are ingested. The refined geometry, the space group chosen and the merged statistics are unchanged. * Faster spot finding and indexing, on the broker as well as in rugnux; the spots found and the lattices indexed are unchanged. * A run reserves substantially less GPU memory: nothing is allocated for buffers that are never read, and a worker builds only the engines it uses. * rugnux: with `-N` left at its default the per-image loop of `--mode mx` uses at most 16 workers per GPU, rather than one per hardware thread; an explicit `-N` is obeyed as given. * CUDA 12 builds now contain device code for Volta, so the RHEL 8 packages and the portable Linux `.tgz` run on a V100; the CUDA 13 artefacts (RHEL 9, Ubuntu, Windows) remain Turing and newer. * The build resolves a single Eigen for the whole project, and refuses to configure if Ceres picks up a different one; a build that mixed two Eigen versions was undefined behaviour and crashed at -O2. * Documentation: a security page, and the supported GPU generations and minimum NVIDIA driver version of every released artefact. **Breaking change to OpenAPI** - regenerate the client (`jfjoch-client` 1.0.0-rc.162, `frontend/src/client`): * `dataset_settings.images_per_file` is no longer `default: 1000` and no longer accepts `0`; it is optional, and its minimum is 1. A client sending `0` (previously "one file for the whole run") is now rejected - omit the field instead, which for a rotation sweep gives the same single file. * `file_writer_format` now defaults to `NXmxVDS`, matching the server's own default and the layout recommended for DIALS, XDS and CrystFEL. A generated client that fills in schema defaults and does not set the format explicitly will write VDS masters where it previously wrote legacy ones; set `NXmxLegacy` explicitly to keep them. --------- Co-authored-by: jungfrau <jungfrau@mx-aare-test.psi.ch> Reviewed-on: #72 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
126 lines
7.3 KiB
C++
126 lines
7.3 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 = 256;
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int reduce_blocks = 0; // global-atomics fallback
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int reduce_blocks_plain = 0; // as many blocks as actually fit, per shared-memory footprint
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int reduce_blocks_clip = 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> host_bkg; // clipped per-ring mean, NaN where the ring is too sparse to trust
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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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// Every per-ring array above is a device-to-host copy once per frame. A D2H copy into PAGEABLE
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// memory blocks the host until it completes, whatever stream it was issued on - which would stall
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// Detect() between the plain pass and the clip passes, with the device then idle while the host
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// enqueues them. Pinning the destinations makes the copies genuinely asynchronous, as the
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// azimuthal-integration engine already does with its own.
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CudaRegisteredVector<unsigned long long> host_sum_reg;
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CudaRegisteredVector<unsigned long long> host_sum2_reg;
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CudaRegisteredVector<uint32_t> host_count_reg;
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CudaRegisteredVector<float> prof_sum_reg;
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CudaRegisteredVector<float> prof_sum2_reg;
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CudaRegisteredVector<uint32_t> prof_count_reg;
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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 SetResolutionMaskBits(const std::vector<uint32_t> &packed_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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[[nodiscard]] const std::vector<float> &GetRingBackground() const override { return host_bkg; }
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};
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