8 Commits

Include renames

Author	SHA1	Message	Date
leonarski_fandClaude Opus 5	bec7e2e922	image_preprocessing: fuse the bitshuffle inverse with preprocessing, and verify the decode ... 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>	2026-08-03 14:13:55 +02:00
leonarski_fandClaude Opus 5	6e4c0ce202	image_preprocessing: decode bitshuffle+LZ4 on the GPU ... The pipeline decompressed each image on the host and uploaded the result. On an 18 Mpx rotation dataset that made the host-to-device copy the bottleneck of the whole per-image loop: nsys puts the copies at 78% of the loop against 39% for every kernel combined - 3600 transfers of 72.4 MB - and they ran at only 12.5 GB/s of an available 27-28 because the host-side decompression was itself saturating host memory bandwidth. The GPU was mostly waiting. So the compressed chunk goes across instead, about 4 MB rather than 72 MB, and is decoded on the device. That removes the transfer and the host decompression that was throttling it, in one change. Measured on an idle machine, a run goes from 45.11 s to 24.97 s - 1.81x - with the merged output unchanged. THE APPROACH IS JON WRIGHT'S (ESRF): "Experiences with GPU decompression for bitshuffle + LZ4 data", HDF5 User Group 2021, and github.com/jonwright/ bslz4decoders. The kernels here are ours, but the idea and the demonstration that it is worth doing are his. Cited in docs/ACKNOWLEDGEMENT.md and in the new section 0 of docs/CPU_DATA_ANALYSIS.md. Two kernels mirror the CPU decoder. LZ4 runs one WARP per bitshuffle block: every lane parses the same sequence stream (a broadcast read, no divergence) and the literal and match copies are split across the 32 lanes so the stores coalesce; an overlapping match is treated as a pattern of period offset sourced from bytes that already precede the write position, which keeps it parallel rather than a serial byte loop. One thread per block instead measured 13x slower. The bitshuffle inverse then un-transposes each byte-plane through shared memory and interleaves the planes back into elements. Only BSHUF_LZ4 is decoded on the device. The zstd variants have no device decoder, and neither has an uncompressed or float image; Supports() returns false for those and the caller decompresses on the host exactly as before. The fallback is explicit, so a format we cannot decode on the device is a slower path and never a wrong answer. Tests hold the device decoder against the CPU one byte for byte, on data from the production compressor, for every element size the detectors emit - including the 8-bit DECTRIS modes, which take bitshuf_decode_block's separate elem_size == 1 branch - plus a many-block frame, the formats it must decline, and malformed containers, which must throw rather than run off a buffer. Battery: 37 crystals, no failures, identical to the host-decode run. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-03 00:16:36 +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