7 Commits

Include renames

Author	SHA1	Message	Date
leonarski_fandClaude Opus 5	e4d70f0e55	image_preprocessing: inline the buffer accessors ... operator[], size(), data() and getBuffer() are one-line accessors that were defined in the .cpp. The build sets no link-time optimisation, so out of line each of them is a real call - once per pixel, from the CPU preprocessor, the CPU azimuthal integrator and the CPU spot finder - and they stop those loops vectorising at all. They show up in a profile directly: about six per cent of a whole azimuthal-integration-only run is spent in the call overhead of two accessors that do nothing but index a vector. Moving them into the header retires 30% fewer instructions on that run and takes the per-image CPU cost on a GPU-less pass from 34.6 to 24.2 ms, with the output bit for bit unchanged - same observation count, same cell, same merge statistics. It is worth nothing on the GPU path, where the image stays on the device, and everything on the paths that have no GPU to fall back on. This also explains a measurement that had been blamed on the pixel mask being a vector<bool>: a microbenchmark of that loop indexed a raw pointer and came out far faster than the same loop in the binary, and the difference was this call, not the mask. Measured properly the mask costs about 14% single-threaded rather than the 41% claimed, and at the thread counts this actually runs at the bit mask is FASTER than the byte mask it was proposed to become, because it moves eight times less traffic and the loop is bandwidth bound. That change should not be made. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-02 11:20:31 +02:00
leonarski_fandClaude Opus 5	6e805f53c0	image_analysis: stop paying for work that is thrown away ... Three independent costs, each measured, none changing a result. Across the 37-crystal regression set the run time halves (median per crystal 2.0x, total 2.3x) and every crystal's merge statistics are unchanged. The image copy back from the device moved the whole preprocessed frame - 72 MB on a large detector, every frame, per worker - to serve a single host consumer that reads only the strong pixels, at most a few hundred kilobytes of it. Give the buffer a Gather() so that consumer asks for the values it actually wants (a host loop on the CPU, a small kernel on the GPU), and copy the frame back only when a CPU spot finder will genuinely read it. The copy the other way was worse: it came from an unregistered vector, so the driver staged it through its own pinned pool with a host-side memcpy on the calling thread, which does not overlap and collapses under concurrency - 11.6 GB/s at one worker, 1.6 GB/s at eight. That, not any hardware limit, is why throughput stopped improving past four to eight workers. Pinning the decompression buffer once per worker fixes it: on a 18 Mpx dataset the image loop goes from 13.6 to 7.9 ms per image at 32 workers, and 32 workers now beat 8 instead of losing to them. Ceres was computing seventeen partial derivatives where five are free. The per-image rotation refinement frees the beam and the orientation and holds distance, detector angles, rotation axis and cell constant, but the cost function declared all seven blocks, so every residual evaluated in Jet<17> arithmetic. A residual exposing only the two free blocks - the same arithmetic, the constants baked in - halves refinement, and it is exact rather than merely close: dual coordinates evolve independently, so the residuals and the free Jacobian columns are unchanged bit for bit. The merge sorted an index array with a comparator that dereferenced a 1.6 GB array of 72-byte records, i.e. a random walk over memory, single-threaded, twice per two-pass run. Sorting a packed key instead is 2.4x. French-Wilson allocated its integration scratch per reflection and ran serially; it now takes caller-owned scratch and runs over chunks, 4.2x. The correction surfaces re-tested every observation for usability and parity on each of ~22 passes and re-allocated their accumulators each time; bucket the indices once and hoist the buffers. Also convert std::round to std::rint where the rounded value only ever enters a squared residual. The tie rules differ - away from zero against to even - so this is safe exactly where a tie flips the sign but not the magnitude, and unsafe wherever the value becomes a Miller index; those sites keep std::round. Verified over all 2^32 float bit patterns: 8388608 exact ties exist, and the squared residual is bitwise equal for every one of them. Worth little on its own here, because the rounding that dominates is in candidate refinement, where the value is an index and the substitution is not available. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-01 21:35:28 +02:00
leonarski_fandClaude Opus 5	0b1fb6c870	image_analysis: share the read-only GPU lookup tables per device ... One analysis engine is built per worker thread, and each uploaded its own copy of tables that are pure functions of the detector geometry: the pixel -> azimuthal bin map and the per-pixel corrections (both in AzIntEngineGPU AND again in AdaptiveSpotFinderGPU, from the same mapping), plus the pixel mask. On an 18 Mpx detector that is ~224 MB per worker; with 32 workers ~7 GB of device memory held 32 identical copies. Upload each table once per GPU instead and hand every engine on that device a shared pointer to it. The cache is keyed by (device, source-vector address) because workers are pinned round-robin across GPUs, so on a multi-GPU node each device keeps its own copy - a kernel may only read memory resident on the device it runs on - and the table is freed on the device that allocated it. Entries are held weakly, so a table goes away with the last engine using it. Measured on an 18 Mpx detector, 32 worker threads, 16 GB card: the stills path went from exhausting the card (OOM in de-novo indexing) to 8.6 GB peak, and a normal rotation run from 14.6 GB to 7.4 GB - it had been running within 1.6 GB of the limit, so any larger detector or second GPU consumer would have tipped it over. Per-worker footprint drops 403 -> 173 MB. Merge statistics are unchanged on a six-crystal regression subset, including two-pass runs where the second pass rebuilds the mapping on refined geometry, and wall time is unchanged (13.5-13.8 s vs 13.8-14.1 s). Also take the launch configuration from the current device rather than device 0 in AzIntEngineGPU and ImagePreprocessorGPU: with round-robin pinning, device 0's SM count and shared-memory size can belong to a different card than the one the kernels use. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-07-31 18:41:27 +02:00
leonarski_fandClaude Opus 5	bf866a0d4c	CUDA: the engines' setup copies belong on the engine's stream ... Making the worker streams non-blocking removed the implicit ordering that the constructors were still relying on. Each engine uploads its static inputs - the pixel mask, the pixel-to-bin map, the corrections, the ROI map - with a blocking NULL-stream cudaMemcpy, and then reads them from kernels on its own stream. A pageable host-to-device cudaMemcpy returns once the source has been staged, with the DMA still in flight, and a non-blocking stream no longer waits for the NULL stream. The failure mode is a silently unapplied mask or a stale mapping, not a crash, so it would not have announced itself. Put them on the stream the engine already owns, and synchronise once at the end of the constructor - that is required for the preprocessor, whose source is a local vector, and leaves the others settled rather than in flight for the cost of one one-time sync. The GPU spot-finder test uploaded its image the same way. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-07-31 11:51:22 +02:00
leonarski_fandClaude Opus 5	2be8680422	CUDA: let worker streams run concurrently ... Every per-thread stream was created with cudaStreamDefault, and the 20 MB raw image upload went to the legacy NULL stream. A NULL-stream operation implicitly synchronises with every blocking stream in the process, so with one engine per worker thread no two workers' GPU work could ever overlap - the whole GPU pipeline ran serially however many threads were asked for. Create the streams non-blocking and put the upload on the engine's own stream. Measured on 2000 serial stills, interleaved, medians of three: 24.6 -> 19.2 s at -N 32 (-22%), 32.7 -> 21.0 s at -N 16 (-36%), CPU utilisation 436-570% -> 723-859%. Output bit-identical - same observations, uniques, completeness, R-meas, CC1/2, error model and cell. The stream is synchronised at the end of the same function, so the ordering the code relies on is unchanged. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-07-30 23:05:11 +02:00
leonarski_f	54c0100e8e	v1.0.0-rc.157 (#67) ... This is an UNSTABLE release. It includes many experimental features, as well as many AI generated fixes. We recommend using rc.152 for production use. * rugnux: Rebrand the offline data-processing subsystem as `rugnux` and consolidate all offline analysis into the single `rugnux` binary - `jfjoch_process` is now `rugnux`, the former `jfjoch_azint` is now `rugnux --azint-only`, and `jfjoch_scale` is now `rugnux --scale` (see the new docs/NAMING.md and docs/RUGNUX.md). Scaling and merging are on by default for rotation and stills (`--no-merge` disables them), replacing the previous opt-in `-M, --scale-merge`. * rugnux: CLI fixes - default `-N` to all hardware threads, parse numeric option arguments strictly (reject non-numeric or trailing input instead of silently yielding 0), require `--wavelength > 0`, and correct the reproduced command line and `--scale` reference-cell handling. * rugnux: De-novo space-group improvements - recover genuine high symmetry and centred Bravais lattices from intensities, add an automatic CC1/2 high-resolution cutoff, and report L-test twinning statistics. * rugnux: Index weakly-diffracting low-resolution rotation data that previously failed (e.g. F-cubic crystals that diffract only to ~4 A on a detector reaching ~1.5 A). The per-frame indexing gate now measures the indexed fraction only within the resolution range the lattice actually diffracts to, so the many sub-diffraction ice/noise spots no longer make the fraction floor unreachable; the two-pass first pass tries several image-sampling schemes (spread across the whole rotation vs a consecutive wedge whose native stride keeps a reflection's rocking curve continuous, letting the FFT resolve a long axis) and keeps the one that indexes the most frames; and the de-novo space-group search no longer discards all reflections (and crashes) when every resolution shell falls below <I/sigma> = 1. * rugnux: Lower the low-resolution R-meas for strongly-diffracting rotation data - drop edge-of-sweep truncated fulls whose rocking curve was captured below `--min-captured-fraction` (default 0.7 for rotation), and report R-meas only over the observations kept by outlier rejection (matching XDS). The 0.7 default also strips the partiality-extrapolated fulls that dominate the intensity second moment on weakly-diffracting crystals, so the de-novo space-group search is no longer starved by the error-model I/sigma floor and recovers the correct symmetry (e.g. the F-cubic Benas crystals: Benas_3 -> F432, Benas_7 -> P6122, instead of P4/P1); on the reference battery every other crystal keeps its space group. * rugnux: Write the refined geometry (beam, tilt, axis) to _process.h5 and place non-standard mmCIF items under a reserved `jfjoch` prefix. * jfjoch_broker: Ordinary acquisition failures (receiver/writer/analysis problems, missed packets, writer disconnect) now return to the Idle state with an Error-severity message, so a run can be retried without an expensive re-initialisation; only failures that leave the detector in an undefined state (new JFJochCriticalException, e.g. PCIe/FPGA faults) go to the Error state and force re-initialisation. * jfjoch_broker: A synchronous /start now reports its failure to the HTTP caller instead of returning HTTP 200, and an incomplete or truncated dataset (missing packets, writer disconnect) is reported as an error rather than a "reduce frame rate" warning. * jfjoch_broker: Drop uncollected placeholder rows (number = -1) from the scan_result REST endpoint. * jfjoch_broker: Fix the inverted per-image compression ratio reported by the Lite receiver (was compressed/uncompressed instead of uncompressed/compressed). * jfjoch_broker: Bragg integration adds a quantization-noise variance floor with a box-sum fallback, and treats the type-maximum marker as an invalid pixel for unsigned image types. * jfjoch_writer: Detect file-overwrite conflicts at start for back-channel transports, and reset the writer when end-of-collection finalisation fails. * jfjoch_viewer: Preview overlays follow the geometry (resolution/ROI arcs, true beam centre, predictions, coral secondary-lattice spots, legend), add save-as-JPEG, and fix an HTTP live-follow memory leak. * Frontend: Improved aesthetics and usability, and added in-browser pixel-mask and JUNGFRAU-pedestal visualisation. * CI: Name the Windows installer jfjoch-viewer-* instead of jfjoch-*.Reviewed-on: #67 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>	2026-07-11 07:19:11 +02:00
leonarski_f	c981e1b91c	v1.0.0-rc.137 (#46) ... This is an UNSTABLE release. The release has significant modifications and bug fixes, if things go wrong, it is better to revert to 1.0.0-rc.132. * jfjoch_broker: Better track time for each operation in the processing stack * jfjoch_broker: Rewrite preprocessing of diffraction images in the non-FPGA workflow to better use GPUs (work in progress) * jfjoch_broker: Remove ROI calculation in the non-FPGA workflow (work in progress) * jfjoch_viewer: Toolbar displays image number starting from 1 (instead of 0) Reviewed-on: #46	2026-04-25 19:59:21 +02:00