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Jungfraujoch/image_analysis/hot_pixels/HotPixelsGPU.h
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leonarski_fandFilip Leonarski 19b135186d
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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

108 lines
5.3 KiB
C++

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#pragma once
#include <cstdint>
#include <memory>
#include <mutex>
#include <vector>
#include "../indexing/CUDAMemHelpers.h"
// What a frame's levels and lit thresholds are made of (HotPixelFinder's constants), handed to the
// device so the two halves cannot drift apart.
struct HotPixelLevelRules {
int min_sector_pixels;
int min_ring_pixels;
float lit_nsigma;
float lit_offset;
};
// The device half of HotPixelFinder, for frames already preprocessed on the GPU. Everything a frame adds
// stays on the device: the per-frame order statistics (each ring-sector's median, each ring's median
// and median absolute deviation), the levels and thresholds made from them, and the per-pixel and
// per-key sums. Every statistic is an exact order statistic of integers, the threshold is computed
// with the same rounding steps as the host's (see HotPixelFinder::AddLevels) and every sum is an
// integer, so the sums are identical to what HotPixelFinder::AddImage produces. When the mask is read
// only what can decide it comes back: per-ring counts for the chance rate, then the few pixels that can
// be persistent or carry the error value.
class HotPixelFinderGPU {
int device = 0; // the card it was built on, where everything below lives
const size_t npixels;
const size_t nkeys;
const int nrings;
const int sectors;
const HotPixelLevelRules rules;
CudaDevicePtr<int32_t> key; // ring * sectors + sector of each pixel, -1 masked
CudaDevicePtr<uint32_t> pixels_by_key; // the unmasked pixels, grouped by key, in pixel order
CudaDevicePtr<uint32_t> key_begin; // where each key's pixels start in pixels_by_key
// The per-pixel and per-key sums, exactly those of HotPixelFinder.
CudaDevicePtr<uint16_t> n_lit, n_error, n_error_ring_ok;
CudaDevicePtr<int64_t> sum_value, error_level_sum;
CudaDevicePtr<uint32_t> key_frames;
CudaDevicePtr<int64_t> key_level_sum, key_local_level_sum;
// Each pixel's sums are plain read-modify-writes, so the frames add to them one after another:
// every accumulation waits on the device for the one before it (an event, not the host).
std::mutex accumulate_mutex;
cudaEvent_t last_accumulate = nullptr;
public:
// One worker's buffers, on the stream its frames are preprocessed on.
struct Frame {
explicit Frame(std::shared_ptr<CudaStream> stream) : stream(std::move(stream)) {}
// Its frames are queued, not waited for (Add), so the buffers below must not be freed before
// the stream has used them.
~Frame() { if (stream) cudaStreamSynchronize(*stream); }
Frame(Frame &&) = default;
std::shared_ptr<CudaStream> stream;
CudaDevicePtr<uint32_t> count; // valid pixels per key
CudaDevicePtr<int32_t> sector_median; // per key
CudaDevicePtr<int32_t> ring_median, ring_mad;
CudaDevicePtr<int32_t> level;
CudaDevicePtr<float> threshold;
CudaDevicePtr<char> ring_ok;
};
// The keys of HotPixelFinder: one per pixel, and where each key's pixels start among the unmasked
// pixels sorted by key.
HotPixelFinderGPU(const int32_t *key, size_t npixels, const std::vector<uint32_t> &key_begin, int nrings,
int sectors, const HotPixelLevelRules &rules);
~HotPixelFinderGPU();
HotPixelFinderGPU(const HotPixelFinderGPU &) = delete;
HotPixelFinderGPU &operator=(const HotPixelFinderGPU &) = delete;
[[nodiscard]] int GetDevice() const { return device; }
// Add the sums another card accumulated to this one's. Every sum is an integer, so the result does
// not depend on how the frames were split between the cards or on the order they are folded in.
// Called with this finder's card current.
void Fold(HotPixelFinderGPU &other);
// Add one frame, preprocessed on frame.stream, as HotPixelFinder::AddImage does. Queued on that
// stream; nothing is waited for on the host.
void Add(const int32_t *device_image, Frame &frame);
// Per ring, over the pixels lit on no more than half of their valid frames: the lit frames and the
// valid frames, summed (HotPixelFinder::GetMask's chance rate). Waits for every frame added.
void ChanceCounts(std::vector<int64_t> &lit, std::vector<int64_t> &seen);
// The pixels that can be masked: those holding the error value on more than half of `frames`;
// where spacing_ok, those lit on at least min(max(2, k_chance[ring]), valid frames) of at least
// min_valid valid frames - a persistent pixel is lit on at least that many, because one reflection
// explains at least two; and those with no error frame, at least min_valid valid frames and a sum
// at most half of their sector's level sum, which a dead pixel has. For each, its index and
// per-pixel sums; and every key's sums.
struct Candidates {
std::vector<uint32_t> index;
std::vector<uint16_t> n_lit, n_error, n_error_ring_ok;
std::vector<int64_t> sum_value, error_level_sum;
std::vector<uint32_t> key_frames;
std::vector<int64_t> key_level_sum, key_local_level_sum;
};
Candidates GetCandidates(uint32_t frames, int min_valid, bool spacing_ok, const std::vector<int> &k_chance);
};