leonarski_fandClaude Opus 5
bec7e2e922
The device decoder was byte-exact on every valid input - 994 production-compressed images, 927 hand-built LZ4 blocks covering engineered (offset, matchlen) pairs across the overlap branch boundary, 18000 repeat decodes, sanitizer-clean - and an audit against LZ4_decompress_generic could not construct a valid block it mis-decodes. What it did not do was notice when the input was NOT valid, and that mattered more than it looks: the decode buffers are reused frame to frame, so a block that stopped early left the PREVIOUS image in place, and in the bitshuffled layout the untouched tail is the most significant byte-plane. A corrupt chunk therefore did not look like a missing corner. It looked like thousands of real pixels several powers of two too bright, fed to spot finding with no diagnostic, where the host decoder had raised an error. So the kernel now flags a block that fails to reach its declared length while consuming exactly its payload, and the host turns that into an exception once the caller has synchronised. Reads are clamped against the end of the payload as well as the output, both length chains are bounded exactly as read_variable_length bounds them, the two offset bytes are bounded, and LZ4's parsing restrictions are enforced. On the host side a block size that is not a multiple of 8 elements is rejected (it made the un-transpose read uninitialised shared memory), the block count is bounded by what the chunk could hold before it becomes an allocation (twelve header bytes could demand hundreds of MB of pinned memory, permanently, per worker), trailing bytes are rejected, and the stream is synchronised before any throw that happens after work is queued. An image of fewer than 8 elements is all verbatim tail and now decodes rather than throwing. When the device route fails for any reason the host decoder gets its turn, so it costs speed rather than the acquisition. The lanes cooperate on the copies and a later match can read bytes another lane wrote, which since Volta needs an explicit __syncwarp(); it worked only because ptxas happened to reconverge at the post-dominator. The prototype's offset == 1 and power-of-two fast paths are also restored - the shipped kernel ran a runtime modulo, an emulated 32-bit division per output byte, on the path its own comment calls the common case. The un-transpose is now fused with preprocessing. One thread owns one group of 8 elements across every byte-plane, so once it has transposed its 8 bytes out of each plane it holds 8 complete elements and emits 8 finished int32 pixels with the mask, the error marker, the saturation cap and the statistics applied. The decompressed image is never materialised: 0.623 -> 0.411 ms/frame at 18 Mpx, 0.523 -> 0.340 with 8 concurrent workers. Staging nothing in shared memory also drops the 48 kB ceiling, which had made any file whose bitshuffle blocks exceed it a hard failure; 64 kB blocks now decode. gpu_compressed is sized from the chunk with grow-on-demand instead of from the uncompressed size - it was reserving ~73 MB per worker to hold ~4 MB. Measured on a 1630x1553 uint32 rotation set at -N 32, peak GPU memory falls 3756 -> 3084 MiB; the same model gives ~144 MB per worker on an 18 Mpx frame. Decoding on the device also stopped reporting a decompression time, which blanked the broker's compression plot trace and filled /entry/profiling/compressionTime with NaN. The decoder brackets the decode with CUDA events and reports it again. Tests: a differential fuzz suite against the CPU decoder - incompressible and highly compressible data, engineered offsets, a size sweep hitting every rem%8 value twice, all six element sizes, an 18 Mpx frame, decoder reuse, concurrency, hand-built LZ4 blocks across the overlap boundary, 26 foreign bitshuffle block sizes from 128 B to 64 kB, corrupt payloads and malformed containers, with a coverage report that proves which LZ4 paths were reached rather than assuming it. Plus the fused path held byte for byte against ImagePreprocessorCPU, statistics included, and against the host-upload path on the same frame. Battery: 37 crystals, every merged number identical to the host-decode run. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
91 lines
4.3 KiB
C++
91 lines
4.3 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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std::unique_ptr<AzIntEngine> azint;
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std::unique_ptr<ROIIntegration> roi;
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std::unique_ptr<ImageSpotFinder> spotFinder;
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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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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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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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std::vector<bool> 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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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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};
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