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Jungfraujoch/image_analysis/MXAnalysisWithoutFPGA.cpp
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leonarski_fandClaude Opus 5 373fda05a4 CUDA: a handled failure discharges the error it leaves behind 
rc.172 stopped two CUDA failures from staying behind as the thread's last error, both inside
CUDAMemHelpers.h: a pooled allocation that fell back to cudaMalloc, and a stream that could not be
created. The reason those mattered was general, and four other places have it too.

Nothing but cudaGetLastError() takes an error away. A later successful call neither clears it nor
returns it - measured: after a cudaMalloc that cannot be served, cudaStreamSynchronize,
cudaMemcpyAsync and a small cudaMalloc all report success, and the "out of memory" is still waiting
for whoever calls cudaGetLastError() next. In this code that is the check after a kernel launch. So
wherever a CUDA failure is caught and the work carries on by another route, the error it left is
reported over the route that went fine:

  - the device decoder in MXAnalysisWithoutFPGA::Analyze, whose failure is answered by decompressing
    on the host - and the host route runs kernels of its own. This is the broker's path, and it is
    the pair of log lines a long-running broker showed, one warning naming the device route and one
    fatal error naming nothing.
  - the indexer worker, which turns a failed attempt into a result and goes on to the next frame -
    on the same thread, so one failure would read as an indexer that never works again.
  - the beam stop's GPU accumulate and the FFT beam-centre capture, both of which fall back to the
    host.

Each discharges it now, through cuda_clear_error() in CUDAWrapper (a no-op without CUDA). A sticky
error is not cleared by this and nothing here pretends otherwise.

tests/CudaHandledErrorTest.cpp: that a failed allocation leaves an error which survives later
successful work until it is cleared, and that an image whose device decode fails comes out of
Analyze whole and leaves nothing behind for the next one.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013nW6FNRP1bBJJ8pfHiByAT
2026-09-20 20:10:43 +02:00

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// SPDX-FileCopyrightText: 2024 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#include "MXAnalysisWithoutFPGA.h"
#include <algorithm>
#include <spdlog/spdlog.h>
#include "spot_finding/StrongPixelSet.h"
#include "../compression/JFJochDecompress.h"
#include "../common/CUDAWrapper.h"
#include "spot_finding/SpotUtils.h"
#include "bragg_prediction/BraggPredictionFactory.h"
#include "image_preprocessing/ImagePreprocessorCPU.h"
#include "azint/AzIntEngineCPU.h"
#include "roi/ROIIntegrationCPU.h"
#include "spot_finding/ImageSpotFinderCPU.h"
#include "spot_finding/AdaptiveSpotFinderCPU.h"
#include "bragg_integration/BraggIntegrationEngineCPU.h"
#ifdef JFJOCH_USE_CUDA
#include "azint/AzIntEngineGPU.h"
#include "roi/ROIIntegrationGPU.h"
#include "spot_finding/ImageSpotFinderGPU.h"
#include "spot_finding/AdaptiveSpotFinderGPU.h"
#include "image_preprocessing/ImagePreprocessorGPU.h"
#include "image_preprocessing/ImagePreprocessorBufferGPU.h"
#include "bragg_integration/BraggIntegrationEngineGPU.h"
#include "../common/CUDAWrapper.h"
#endif
MXAnalysisWithoutFPGA::MXAnalysisWithoutFPGA(const DiffractionExperiment &in_experiment,
const AzimuthalIntegrationMapping &in_integration,
const PixelMask &in_mask,
IndexAndRefine &in_indexer,
bool in_enable_fused_adaptive_gpu)
: experiment(in_experiment),
integration(in_integration),
enable_fused_adaptive_gpu(in_enable_fused_adaptive_gpu),
npixels(experiment.GetPixelsNum()),
xpixels(experiment.GetXPixelsNum()),
indexer(in_indexer),
prediction(CreateBraggPrediction(experiment.IsRotationIndexing())),
mask(in_mask),
mask_high_res(-1),
mask_low_res(-1) {
#ifdef JFJOCH_USE_CUDA
if (get_gpu_count() == 0) {
#endif
preprocessor_buffer = std::make_unique<ImagePreprocessorBuffer>(experiment.GetPixelsNum());
preprocessor = std::make_unique<ImagePreprocessorCPU>(in_experiment, in_mask);
bragg_engine = std::make_unique<BraggIntegrationEngineCPU>(in_experiment);
if (experiment.ROI().size() >= 1)
roi = std::make_unique<ROIIntegrationCPU>(experiment);
#ifdef JFJOCH_USE_CUDA
} else {
stream = std::make_shared<CudaStream>();
// The host copy of the preprocessed image is only read when a CPU engine wants it, which is
// the same condition that drives copy_image_to_host below. Skipping it also skips page-locking
// 4 bytes per pixel per worker.
preprocessor_buffer = std::make_unique<ImagePreprocessorBufferGPU>(
experiment.GetPixelsNum(), /*host_mirror=*/!enable_fused_adaptive_gpu);
// The preprocessed image only has to come back to the host if a CPU engine reads it. Every
// engine built below runs on the GPU, except the CPU adaptive finder that is kept when the fused
// GPU engine is off - so that is the one case that needs the copy. Every caller currently passes
// enable_fused_adaptive_gpu = true, so on the GPU path the copy is off in practice.
preprocessor = std::make_unique<ImagePreprocessorGPU>(in_experiment, in_mask, stream,
/*copy_image_to_host=*/!enable_fused_adaptive_gpu);
bragg_engine = std::make_unique<BraggIntegrationEngineGPU>(in_experiment, stream);
if (experiment.ROI().size() >= 1)
roi = std::make_unique<ROIIntegrationGPU>(experiment, stream);
if (enable_fused_adaptive_gpu) {
// One GPU engine that computes the azimuthal profile and the adaptive spot mask in a single
// image pass. fused_adaptive aliases it so Analyze() can lift the profile out of it.
auto fused = std::make_unique<AdaptiveSpotFinderGPU>(integration, stream);
fused_adaptive = fused.get();
adaptiveSpotFinder = std::move(fused);
}
}
#endif
if (!adaptiveSpotFinder)
adaptiveSpotFinder = std::make_unique<AdaptiveSpotFinderCPU>(integration);
}
void MXAnalysisWithoutFPGA::Analyze(DataMessage &output,
AzimuthalIntegrationProfile &profile,
const SpotFindingSettings &spot_finding_settings) {
if ((output.image.GetWidth() != xpixels)
|| (output.image.GetWidth() * output.image.GetHeight() != npixels))
throw JFJochException(JFJochExceptionCategory::InputParameterInvalid,
"Mismatch in pixel size");
// Decompress on the device where the preprocessor can, so only the compressed chunk crosses PCIe
// and the host does no decompression at all. AnalyzeCompressed says whether it took the image;
// when it declines (a CPU preprocessor, or an algorithm with no device decoder) fall through to
// the host route unchanged. The two produce the same preprocessed image.
const auto compression_start_time = std::chrono::steady_clock::now();
ImageStatistics ret{};
bool decoded_on_device = false;
try {
decoded_on_device = preprocessor->AnalyzeCompressed(*preprocessor_buffer, output.image, ret);
} catch (const JFJochException &e) {
// The device route must never be the reason a frame fails: whatever it could not handle, the
// host decoder gets its turn. If the data really is bad the host throws too and the caller
// sees the same error it saw before any of this existed - but a GPU-side problem costs speed
// rather than the acquisition.
spdlog::warn("Device decoding failed ({}), falling back to host decompression", e.what());
decoded_on_device = false;
// Handled here, so it must not stay behind as the thread's last error: the host route below
// runs kernels of its own and checks cudaGetLastError() after them, which would report this
// failure again - fatally - over an image that decoded perfectly well on the host.
cuda_clear_error();
}
const auto compression_end_time = std::chrono::steady_clock::now();
if (!decoded_on_device) {
const uint8_t *image_ptr = Decompress(output.image);
const auto decompressed_time = std::chrono::steady_clock::now();
if (output.image.GetCompressionAlgorithm() != CompressionAlgorithm::NO_COMPRESSION)
output.compression_time_s = std::chrono::duration<float>(decompressed_time - compression_start_time).count();
const auto preprocessing_start_time = std::chrono::steady_clock::now();
ret = preprocessor->Analyze(*preprocessor_buffer, image_ptr, output.image.GetMode());
const auto preprocessing_end_time = std::chrono::steady_clock::now();
output.preprocessing_time_s = std::chrono::duration<float>(preprocessing_end_time - preprocessing_start_time).count();
} else {
// Decode and preprocess are one device operation here, but the decompression is still a real,
// separately measurable cost - the decoder brackets it with CUDA events - so it is still
// reported as one. Leaving compression_time_s unset instead would blank the broker's
// "compression" plot trace and fill /entry/profiling/compressionTime with NaN.
const float total_s = std::chrono::duration<float>(compression_end_time - compression_start_time).count();
const float decompress_s = std::min(preprocessor->GetLastDecompressionTime_s(), total_s);
output.compression_time_s = decompress_s;
output.preprocessing_time_s = total_s - decompress_s;
}
// The fused GPU engine (rugnux offline, GPU, adaptive detection) produces the azimuthal profile as
// a byproduct of spot finding, so the separate azint pass is skipped in that case and the profile is
// lifted out of the finder below.
const bool fused = enable_fused_adaptive_gpu && spot_finding_settings.enable
&& spot_finding_settings.adaptive_threshold && fused_adaptive != nullptr;
if (!fused) {
const auto azint_start_time = std::chrono::steady_clock::now();
AzInt().Run(*preprocessor_buffer, profile);
const auto azint_end_time = std::chrono::steady_clock::now();
output.azint_time_s = std::chrono::duration<float>(azint_end_time - azint_start_time).count();
}
if (roi)
roi->Run(*preprocessor_buffer, output.roi);
if (spot_finding_settings.enable) {
// Update resolution mask
if (mask_high_res != spot_finding_settings.high_resolution_limit
|| mask_low_res != spot_finding_settings.low_resolution_limit)
UpdateMaskResolution(spot_finding_settings);
ImageSpotFinder &finder = spot_finding_settings.adaptive_threshold
? static_cast<ImageSpotFinder &>(*adaptiveSpotFinder)
: FixedThresholdFinder();
const auto integrate_fn = [this](const std::vector<Reflection> &predicted, size_t npredicted,
int64_t image_number) {
return bragg_engine->Run(*preprocessor_buffer, predicted, npredicted, image_number);
};
// The radial background correction has to be decided BEFORE this image is integrated, so the
// ice score is taken here rather than with the other per-image quantities at the end of the
// function. It needs the peak-excluded per-ring background, which the adaptive finder has as
// soon as it has detected - so this must be called after detection and before integration.
// Where no such background exists (no adaptive finder), auto leaves the correction off: the
// plain profile carries the Bragg peaks and cannot support an absolute threshold.
const auto decide_radial_background = [this, &spot_finding_settings, &output]() {
if (!bragg_engine->IsBackgroundRadialAuto())
return;
// Same condition as the score at the end of this function: the adaptive finder holds its
// ring background from whenever it last ran, so requiring that it ran for THIS image is
// what keeps a stale curve out.
if (!spot_finding_settings.adaptive_threshold)
return;
const std::vector<float> &ring_bkg = adaptiveSpotFinder->GetRingBackground();
if (ring_bkg.empty())
return;
output.ice_ring_score = AzimuthalIntegrationProfile::IceRingScore(
ring_bkg, integration.GetQBinCount(), integration.Settings(),
spot_finding_settings.ice_ring_width_Q_recipA);
bragg_engine->BackgroundRadial(*output.ice_ring_score
>= experiment.GetScalingSettings().GetIceMinScore());
};
// A missing min-pix (std::nullopt) means "choose it per image". This applies only to the stills
// indexing path (each frame is indexed independently); rotation indexing builds one lattice from
// all frames, so it keeps the fixed min-pix and the single-pass finder.
const bool adaptive_min_pix = !spot_finding_settings.min_pix_per_spot.has_value()
&& spot_finding_settings.indexing
&& !experiment.IsRotationIndexing();
if (adaptive_min_pix) {
// Choose the per-image min-pix adaptively instead of a fixed one. min-pix filters connected
// components AFTER detection, so BOTH the detection (the expensive per-pixel pass) and the
// connected-component search run ONCE and only the filter is repeated; the azimuthal
// profile is the one Detect() computed.
// Index at 3/2/1 (index-only, no integration/accumulation), keep whichever maximises
// n_indexed^2 / n_total (indexed count weighted by indexed fraction) together with its spot
// list, and integrate that one.
const auto detect_start_time = std::chrono::steady_clock::now();
finder.Detect(*preprocessor_buffer, spot_finding_settings);
const auto &components = finder.ExtractComponents(*preprocessor_buffer, spot_finding_settings);
float spot_finding_time_s =
std::chrono::duration<float>(std::chrono::steady_clock::now() - detect_start_time).count();
float indexing_time_s = 0.0f;
SpotFindingSettings s = spot_finding_settings;
std::vector<DiffractionSpot> best_spots;
int best_mp = 0;
double best_score = -1.0;
for (int mp : {3, 2, 1}) {
s.min_pix_per_spot = mp;
const auto extract_start_time = std::chrono::steady_clock::now();
std::vector<DiffractionSpot> spots = ImageSpotFinder::Filter(components, s);
spot_finding_time_s +=
std::chrono::duration<float>(std::chrono::steady_clock::now() - extract_start_time).count();
SpotAnalyze(experiment, s, spots, output);
const bool indexed = indexer.IndexFrameOnly(output, s);
indexing_time_s += output.indexing_time_s.value_or(0.0f);
if (indexed) {
const double n_idx = static_cast<double>(output.spot_count_indexed.value_or(0));
const double n_tot = static_cast<double>(std::max<int64_t>(1, output.spot_count.value_or(1)));
const double score = n_idx * n_idx / n_tot;
if (score > best_score) {
best_score = score;
best_mp = mp;
best_spots = std::move(spots);
}
}
}
if (best_mp != 0) {
// Index and integrate the winning spot list; no spot finding left to do.
s.min_pix_per_spot = best_mp;
SpotAnalyze(experiment, s, best_spots, output);
decide_radial_background();
indexer.ProcessImage(output, s, *prediction, integrate_fn);
indexing_time_s += output.indexing_time_s.value_or(0.0f);
}
// Each indexer call reports only its own time, so the escalation's total is summed here.
output.spot_finding_time_s = spot_finding_time_s;
output.indexing_time_s = indexing_time_s;
} else {
const auto spot_finding_start_time = std::chrono::steady_clock::now();
const std::vector<DiffractionSpot> spots = finder.Run(*preprocessor_buffer, spot_finding_settings);
SpotAnalyze(experiment, spot_finding_settings, spots, output);
output.spot_finding_time_s = std::chrono::duration<float>(std::chrono::steady_clock::now() - spot_finding_start_time).count();
decide_radial_background();
if (spot_finding_settings.indexing)
indexer.ProcessImage(output, spot_finding_settings, *prediction, integrate_fn);
}
// Recorded whichever way the frame went. A frame holding StrongPixelLimit of them is given up
// on and reports no spots at all, and this is the only thing that tells such a frame from one
// that did not diffract.
output.strong_pixel_count = finder.StrongPixelCount();
#ifdef JFJOCH_USE_CUDA
if (fused) {
// Lift the azimuthal profile the fused engine computed in the same detection pass; its azint
// cost is folded into spot_finding_time_s above.
profile.Clear(integration);
profile += fused_adaptive->GetProfile();
output.azint_time_s = 0.0f;
}
#endif
}
output.max_viable_pixel_value = ret.max_value;
output.min_viable_pixel_value = ret.min_value;
output.error_pixel_count = ret.error_pixel_count;
output.saturated_pixel_count = ret.saturated_pixel_count;
output.az_int_profile = profile.GetResult();
output.az_int_profile_count = profile.GetPixelCount();
output.az_int_profile_std = profile.GetStd();
output.bkg_estimate = profile.GetBkgEstimate(integration.Settings());
// The ice score wants a radial profile with the Bragg peaks taken OUT of it. The azimuthal profile
// is a plain per-ring mean, so a strong low-resolution reflection landing in a ring's bin is
// indistinguishable from ice sitting there - measured, that alone lifts clean crystals to a score
// of 1.5-4.2, right into the range real ice occupies. The adaptive spot finder already computes
// exactly what is wanted: a sigma-clipped per-ring background, in the same bins, from which the
// peaks have been removed (an ice ring is azimuthally smooth, so it survives the clip). It is in
// raw counts rather than corrected ones, which the score does not care about - it is a ratio to the
// background interpolated under the ring, and the corrections are smooth in radius.
const std::vector<float> &ring_bkg = adaptiveSpotFinder->GetRingBackground();
const bool have_ring_bkg = spot_finding_settings.enable && spot_finding_settings.adaptive_threshold
&& !ring_bkg.empty();
output.ice_ring_score = AzimuthalIntegrationProfile::IceRingScore(
have_ring_bkg ? ring_bkg : profile.GetResult1D(), integration.GetQBinCount(),
integration.Settings(), spot_finding_settings.ice_ring_width_Q_recipA);
}
ImageSpotFinder &MXAnalysisWithoutFPGA::FixedThresholdFinder() {
if (!spotFinder) {
#ifdef JFJOCH_USE_CUDA
if (stream)
spotFinder = std::make_unique<ImageSpotFinderGPU>(experiment.GetXPixelsNum(),
experiment.GetYPixelsNum(), stream);
else
#endif
spotFinder = std::make_unique<ImageSpotFinderCPU>(experiment.GetXPixelsNum(),
experiment.GetYPixelsNum());
// It missed every mask update that happened before it existed, so it takes the current one
// now. Without this it would find spots outside the resolution limits.
if (mask_resolution)
spotFinder->SetResolutionMaskBits(*mask_resolution);
}
return *spotFinder;
}
AzIntEngine &MXAnalysisWithoutFPGA::AzInt() {
if (!azint) {
#ifdef JFJOCH_USE_CUDA
if (stream)
azint = std::make_unique<AzIntEngineGPU>(integration, stream);
else
#endif
azint = std::make_unique<AzIntEngineCPU>(integration);
}
return *azint;
}
void MXAnalysisWithoutFPGA::RebuildROI() {
if (experiment.ROI().empty()) {
roi.reset();
return;
}
#ifdef JFJOCH_USE_CUDA
if (stream) {
roi = std::make_unique<ROIIntegrationGPU>(experiment, stream);
return;
}
#endif
roi = std::make_unique<ROIIntegrationCPU>(experiment);
}
void MXAnalysisWithoutFPGA::AnalyzeROIOnly(DataMessage &output) {
if ((output.image.GetWidth() != xpixels)
|| (output.image.GetWidth() * output.image.GetHeight() != npixels))
throw JFJochException(JFJochExceptionCategory::InputParameterInvalid,
"Mismatch in pixel size");
const uint8_t *image_ptr = Decompress(output.image);
preprocessor->Analyze(*preprocessor_buffer, image_ptr, output.image.GetMode());
RunROIOnly(output);
}
const uint8_t *MXAnalysisWithoutFPGA::Decompress(const CompressedImage &image) {
// An uncompressed image is read straight out of the message and never touches decompression_buffer,
// so what the upload reads is the reader's own bytes: page-lock those instead. Pageable memory is
// staged by the driver a chunk at a time, which on a 4-byte 16 Mpx frame is 12.9 ms against 4.0 ms
// pinned - and a miniCBF sweep takes this path for every image.
if (image.GetCompressionAlgorithm() == CompressionAlgorithm::NO_COMPRESSION)
preprocessor->PinInputRegion(image.GetCompressed(), image.GetUncompressedSize());
else
preprocessor->PinInputBuffer(decompression_buffer, image.GetUncompressedSize());
return image.GetUncompressedPtr(decompression_buffer);
}
void MXAnalysisWithoutFPGA::RunROIOnly(DataMessage &output) {
output.roi.clear();
if (roi)
roi->Run(*preprocessor_buffer, output.roi);
}
BraggIntegrationCounts MXAnalysisWithoutFPGA::BraggCounts() const {
return bragg_engine ? bragg_engine->Counts() : BraggIntegrationCounts{};
}
void MXAnalysisWithoutFPGA::UpdateMaskResolution(const SpotFindingSettings &settings) {
mask_low_res = settings.low_resolution_limit;
mask_high_res = settings.high_resolution_limit;
// The mask is a pure function of the resolution map and the two limits, so the mapping builds it -
// once for all the workers, which otherwise each walked every pixel of the detector to arrive at
// the same bits.
mask_resolution = integration.ResolutionMaskBits(mask_high_res, mask_low_res);
// The finders keep their own copy (the GPU ones a bit-packed device copy), so the mask is handed
// over here - when the limits change - rather than with every image.
if (spotFinder)
spotFinder->SetResolutionMaskBits(*mask_resolution);
adaptiveSpotFinder->SetResolutionMaskBits(*mask_resolution);
}
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