leonarski_fandClaude Opus 5.5
cd98787728
On the CPU path every image made a separate azimuthal-integration pass (AzIntEngineCPU) over the 72 MB frame although the adaptive finder's first ring pass reads the same pixels in the same order under the same rules (skip the INT32_MIN/MAX sentinels, bins below the mapping's count). That pass now also accumulates the corrected profile - the same statements as AzIntEngineCPU, so the same float sums - and MXAnalysisWithoutFPGA takes the profile from the finder instead of running the separate pass, as the fused GPU engine already does. Only where the azimuthal engine would be the CPU one; the finder the pre-scan uses does not accumulate it. md5-identical p.hkl, p_unmerged.mtz, p_plot.txt and report; CPU-only 163 -> 149 s and 94 -> 91 s. GPU unchanged. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01D1G8gJVAy6gp1K5Dz3NE5C
108 lines
5.5 KiB
C++
108 lines
5.5 KiB
C++
// SPDX-FileCopyrightText: 2024 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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#include <mutex>
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#include "../common/JFJochMessages.h"
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#include "../common/DiffractionExperiment.h"
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#include "../common/AzimuthalIntegrationMapping.h"
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#include "../common/PixelMask.h"
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#include "../common/AzimuthalIntegrationProfile.h"
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#include "bragg_prediction/BraggPrediction.h"
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#include "bragg_integration/BraggIntegrationEngine.h"
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#include "spot_finding/ImageSpotFinder.h"
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#include "spot_finding/AdaptiveSpotFinderCPU.h"
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#include "indexing/IndexerThreadPool.h"
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#include "azint/AzIntEngine.h"
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#include "roi/ROIIntegration.h"
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#include "IndexAndRefine.h"
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#include "image_preprocessing/ImagePreprocessor.h"
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#include "image_preprocessing/ImagePreprocessorBuffer.h"
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class CudaStream;
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class AdaptiveSpotFinderGPU;
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// MXAnalysisWithoutFPGA is not thread safe - it has to owned by a single thread
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class MXAnalysisWithoutFPGA {
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const DiffractionExperiment &experiment;
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const AzimuthalIntegrationMapping &integration;
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std::vector<uint8_t> decompression_buffer;
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std::unique_ptr<ImagePreprocessor> preprocessor;
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size_t npixels;
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size_t xpixels;
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// Built on first use: the fused adaptive finder produces the azimuthal profile as a by-product,
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// so on the rugnux path this engine is constructed and then never run.
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std::unique_ptr<AzIntEngine> azint;
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AzIntEngine &AzInt();
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std::unique_ptr<ROIIntegration> roi;
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// Built on first use. Which finder an image takes arrives with its SpotFindingSettings, and
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// with adaptive detection on - the default everywhere but the broker - this one is never asked
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// for; on the GPU it is ~14 MB and 15 device allocations per worker.
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std::unique_ptr<ImageSpotFinder> spotFinder;
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ImageSpotFinder &FixedThresholdFinder();
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// Self-calibrating finder, used when spot settings request adaptive detection. Kept alongside the
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// default finder because the choice arrives with the per-image settings, not at construction. It is
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// an AdaptiveSpotFinderCPU by default; on the GPU path, when the fused engine is enabled (rugnux
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// offline only), it is instead an AdaptiveSpotFinderGPU that also computes the azimuthal profile,
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// aliased through fused_adaptive so Analyze() can take that profile and skip the separate azint pass.
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std::unique_ptr<ImageSpotFinder> adaptiveSpotFinder;
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AdaptiveSpotFinderGPU *fused_adaptive = nullptr;
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// The CPU finder, where it gives the profile too (the azimuthal integration being on the CPU).
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AdaptiveSpotFinderCPU *fused_adaptive_cpu = nullptr;
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const bool enable_fused_adaptive_gpu;
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IndexAndRefine &indexer;
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std::unique_ptr<BraggPrediction> prediction;
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std::unique_ptr<BraggIntegrationEngine> bragg_engine;
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// What the supercell probe's integrations added to bragg_engine's counts (see BraggCounts).
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BraggIntegrationCounts probe_counts;
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std::unique_ptr<ImagePreprocessorBuffer> preprocessor_buffer;
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const PixelMask &mask;
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// Decompress the image into decompression_buffer (or read it straight from the message, when it is
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// not compressed) and return where it landed.
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const uint8_t *Decompress(const CompressedImage &image);
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// Pixels outside the resolution limits, bit-packed. Built by the integration mapping, which is
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// shared by every worker's engine and hands out the same mask to all of them.
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std::shared_ptr<const std::vector<uint32_t>> mask_resolution;
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// The limits mask_resolution was built for. Kept as the OPTIONAL the caller passed, so an unset
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// high-resolution limit compares equal to itself and the mask is not rebuilt on every image.
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std::optional<float> mask_high_res;
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std::optional<float> mask_low_res;
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void UpdateMaskResolution(const SpotFindingSettings& settings);
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#ifdef JFJOCH_USE_CUDA
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std::shared_ptr<CudaStream> stream; // kept so RebuildROI() can recreate the GPU ROI engine
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#endif
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public:
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// enable_fused_adaptive_gpu turns on the fused GPU azint+adaptive spot finder (only takes effect on
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// the GPU path with adaptive detection). The rugnux offline path and the interactive viewer enable
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// it by default, as does the online receiver. It only changes performance - the fused engine
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// reproduces the CPU finder's spots. Note it also decides whether the preprocessed image is copied
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// back to the host each frame: that copy exists only for a CPU engine to read, and with the flag on
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// no CPU engine is built, so the copy is skipped.
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MXAnalysisWithoutFPGA(const DiffractionExperiment &experiment, const AzimuthalIntegrationMapping &integration,
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const PixelMask &mask, IndexAndRefine &indexer, bool enable_fused_adaptive_gpu = false);
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void Analyze(DataMessage &output, AzimuthalIntegrationProfile &profile, const SpotFindingSettings &spot_finding_settings);
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// Surgical ROI-only paths used when a full re-analysis is not wanted: rebuild the
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// ROI engine after the ROI set changes, recompute ROIs after preprocessing a new
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// image (reanalyze off), or just rerun ROIs on the current preprocessed image (an
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// interactive ROI move). A full Analyze() already computes ROIs, so needs nothing.
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void RebuildROI();
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void AnalyzeROIOnly(DataMessage &output);
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void RunROIOnly(DataMessage &output);
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// What this worker's Bragg integrator counted (BraggIntegrationCounts). Each worker builds its own
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// analysis, so a caller that wants the run's totals sums this over the workers it started.
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[[nodiscard]] BraggIntegrationCounts BraggCounts() const;
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};
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