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Jungfraujoch/image_analysis/image_preprocessing/ImagePreprocessorGPU.h
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v1.0.0-rc.175 (#85)
* Rugnux: Processes a collection live while jfjoch_broker writes it (`rugnux <master.h5> <broker URL>`).
* Rugnux: Processing starts on the first images and confirms its choices once all are in, so analysis can begin during collection.
* Rugnux: Faster processing, especially on servers with several GPUs and CPU sockets and on computers without a GPU (`--no-gpu`, macOS).
* Rugnux: Uses much less GPU memory, so large data sets stay on the GPU of smaller cards, and a data set too large for the card is processed on the CPU instead.
* Rugnux: On macOS, Bragg integration runs on the Apple GPU.
* Rugnux: Better handling of twinning and pseudo-symmetry, with the twin hypothesis written as `<prefix>_twin_<SG>.mtz`, and a wrong `--model` no longer confirms the data or decides the hand.
* Rugnux: `--model` accepts predicted (AlphaFold) models and falls back to molecular replacement when the model is not placed as a rigid body (experimental `--mr` forces it), settling space group, hand and twinning and writing a different answer as `<prefix>_mr.mtz`.
* Rugnux: Molecular replacement is faster and settles the space group in more cases with several copies in the asymmetric unit.
* Rugnux: Deposition-ready mmCIF, a richer report, and numbered steps on the terminal with the full log in `<prefix>_log.txt`; `_P1.mtz`, `--estimate-beam-center` and `--beam-center-search` are removed.
* Rugnux: The beam-stop shadow is found correctly on tilted detectors.
* Rugnux: Stills files that record a stationary goniometer axis get the default stills geometry refinement.
* jfjoch_viewer: "Analyze dataset" uses the same defaults as a bare `rugnux <file>`, and shows the results of processed CBF, marCCD and SMV sweeps.
* jfjoch_viewer: The Files panel refreshes on F5 and every few seconds, and the plot type and other selections stay put during live collection.
* jfjoch_writer: Memory stays bounded and old data files are closed efficiently on long runs, any failure ends the run in an error state the broker is told about, and the master file appears only after its data files; the ZeroMQ pusher logs when a stalled writer makes it drop images.
* Fixes: The TCP image pusher no longer blocks the broker's receiver, GPU out-of-memory failures stop with a clear message, and the XDS plugin masks module and chip edges.

---------

Co-authored-by: Filip Leonarski <jungfrau@filips-mac-mini.home>
Reviewed-on: #85
Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
2026-10-16 15:34:43 +02:00

78 lines
4.6 KiB
C++

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#pragma once
#include <memory>
#include "ImagePreprocessor.h"
#include "BSLZ4DecoderGPU.h"
#include "CBFDecoderGPU.h"
#include "../indexing/CUDAMemHelpers.h"
#include "../indexing/CudaSharedTables.h"
class ImagePreprocessorGPU : public ImagePreprocessor {
std::shared_ptr<CudaStream> stream;
const bool copy_image_to_host;
int threads;
int blocks;
// Geometry-only, so one copy per GPU shared with every other engine on it (CudaSharedTables.h).
std::shared_ptr<CudaDevicePtr<uint8_t>> gpu_mask;
// Landing buffer for the HOST-upload path only. The device-decode path un-transposes straight
// into the preprocessed image, so it never needs this - and at 4 bytes per pixel it is worth a
// frame per worker, so it is allocated on first use rather than always.
CudaDevicePtr<uint8_t> gpu_decompressed_image;
CudaDevicePtr<ImageStatistics> gpu_stats;
PinnedVector<ImageStatistics> cpu_stats;
CudaRegisteredVector<uint8_t> input_reg; // page-locks the caller's decompression buffer
// The uncompressed-image counterpart of input_reg: the region PinInputRegion last page-locked.
const void *pinned_input = nullptr;
size_t pinned_input_bytes = 0;
// Staging for a marCCD frame's stored 16-bit pixels (UINT16_WIDENED), allocated on first use.
CudaHostPtr<uint8_t> host_stored;
// The card reads host memory directly (gpu_direct_host_read()): images are read where they lie,
// nothing is page-locked or staged; only the CBF device decoder still uploads its input.
bool direct_host_read = false;
std::vector<int32_t> cpu_image;
// Built on first use: a decoder that can serve this engine's images, sized to the frame.
std::unique_ptr<BSLZ4DecoderGPU> bslz4_decoder;
std::unique_ptr<CBFDecoderGPU> cbf_decoder;
// Which decoder the last AnalyzeCompressed used, for GetLastDecompressionTime_s.
enum class LastDecode { None, BSLZ4, CBF } last_decode = LastDecode::None;
// The file encodings of the miniCBF and marCCD readers (CompressionAlgorithmEnum.h): the stored
// bytes are uploaded, decoded into gpu_decompressed_image as the int32 image the host would have
// produced, and preprocessed by the same kernel as an uploaded int32 image.
ImageStatistics AnalyzeStoredInt32(ImagePreprocessorBuffer &processed_image, const CompressedImage &image);
template <class T> ImageStatistics Analyze(ImagePreprocessorBuffer &processed_image, const uint8_t *input, T err_value, T sat_value);
// Preprocess the image at `input`: gpu_decompressed_image on the host-upload path, or the host
// buffer itself where the card reads host memory directly. err_value and sat_value are compared
// with the pixel's value, not its bits, so a narrower T can carry the int32 semantics of the image
// it widens to.
template <class T> ImageStatistics AnalyzeOnDevice(ImagePreprocessorBuffer &processed_image, const void *input,
int64_t err_value, int64_t sat_value);
// Preprocess straight out of the bitshuffled bytes (the device-decode path). Same per-pixel
// decision and same statistics as AnalyzeOnDevice, with the un-transpose folded in.
template <class T, int ES> ImageStatistics UntransposeAndAnalyze(ImagePreprocessorBuffer &processed_image,
const BSLZ4ShuffledImage &shuffled,
T err_value, T sat_value);
public:
// copy_image_to_host copies the preprocessed image back after every frame. It is only needed when
// something on the CPU reads it - the GPU engines all work off the device buffer - and at 4 bytes
// per pixel it is the single largest transfer in the pipeline, so the caller says whether it wants it.
ImagePreprocessorGPU(const DiffractionExperiment &experiment, const PixelMask &mask, std::shared_ptr<CudaStream> stream,
bool copy_image_to_host = true);
~ImagePreprocessorGPU() override;
ImageStatistics Analyze(ImagePreprocessorBuffer &processed_image, const uint8_t *decompressed_image, CompressedImageMode image_mode) override;
bool AnalyzeCompressed(ImagePreprocessorBuffer &processed_image, const CompressedImage &image,
ImageStatistics &stats) override;
[[nodiscard]] float GetLastDecompressionTime_s() const override;
void PinInputBuffer(std::vector<uint8_t> &buffer, size_t size) override;
void PinInputRegion(const void *ptr, size_t bytes) override;
};