5 Commits

Include renames

Author	SHA1	Message	Date
leonarski_f	1f8b5b1986	Refinement: a Refit's solvent grid starts around where the context's last one ended ... Logic change (deterministic), kept on evidence. Every Refit searched the whole k_sol/b_sol box - an 88-point coarse pass of plain least squares, then the robust fine pass around its winner. ModelScaleBox (and ModelScaleGPUPool::Fit / ModelScaleGPU::FitSolvent) take an optional start; with one, the coarse pass is skipped and the fine pass is laid around it. Refinement passes the grid winner of the context's previous Refit (before MoveOverallB moves the overall B into b_sol); it travels with a trial's fit, and a trial's first Refit without one (Context::ForgetStarts, which also clears the likelihood's D start) searches the whole box, so a trial still depends on nothing its slot ran before (side by side = one at a time, md5). Evidence (GPU, -N 8): full refinement of the 14 parity sets from their placed models - 12 refined CIFs identical (md5), 9ig7 and 6iu9 differ, final R-work/R-free equal to 4 digits but 6iu9 R-work 0.2911 -> 0.2910; of the 5 timing cases 4 identical, 7d98 (cif start) R-free 0.3096 -> 0.3095; 5f6m MD starts 01-04 median R-free 0.1692 -> 0.1692; 6o2h on the CPU engine identical (md5). The score-only parity table is not touched (one Refit, no start). refit.scale (TIMING sum): 5f6m 1.52 -> 1.10 s. Wall: 5reo 1.68 -> 1.53, 5f6m 2.43 -> 2.24, 9yl4 7.19 -> 7.02, 7d98 3.70 -> 3.65, 5f6m MD start 4.57 -> 4.22 s. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu (cherry picked from commit ff285fb525ecf4b480698d4aafe212a34301a8fe)	2026-10-26 09:16:34 +01:00
leonarski_f	1f1aa18b67	ModelScaleGPU: the robust fit's median of the residuals on the device ... Bitwise. The first robust round copied every fine-pass fit's residuals to the host (up to 25 x n floats, 9 MB at 92k reflections) and took std::nth_element of each there. The median is now a radix selection on the card (one block per fit, four byte-wide passes of integer counts over the float bits - the residuals are not negative, so the bits order as the values): the element nth_element puts at n / 2, the same value; only the fits' c come back. FitSolvent and RobustBatch lose the host threads they took for it. Refined CIF md5 unchanged on 5reo, 5f6m, 9yl4, 7d98, 5f6m MD start. Wall (-N 8, quiet): 5reo 1.72 -> 1.68, 5f6m 2.57 -> 2.43, 9yl4 7.28 -> 7.19, 7d98 3.78 -> 3.70, 5f6m MD start 4.76 -> 4.57 s. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu (cherry picked from commit dc53896c335fdabdf4d417323038e05b8b63535d)	2026-10-26 09:16:34 +01:00
leonarski_f	46ad2c10ed	Refinement GPU: the robust scale fit on the card, the pool back in every trial slot ... The score's robust fine pass (pseudo-Huber IRLS of ModelScaleBox::robust) on the device, so that refinement on a GPU no longer fits its scale on the host every cycle - with the trials side by side that was a robust grid search on one thread per slot (13-133 s per refinement on the seven timing sets, against 1.7-8 s before the score commit). - ModelScaleGPU: the weights multiply the WSSR and J^T J / J^T r in scale_sums (a weight row per fit, reserved at the first robust fit); robust_residuals / robust_weights kernels; the median of each fit's first residuals taken on the host as FitModelScale takes it (nth_element at n/2, on the fits' threads); LevMarBatch split out of FitBatch so that the rounds restart from where a fit stands. FitSolvent(..., robust) refits the coarse winner in the fine pass, as FitModelScale's robust fit does; the plain fit is unchanged. - ModelScaleGPUPool::Fit(..., robust); its streams at the highest priority: a fit is a chain of small launches the host waits on, and on a card shared with the trials' long kernels each wait queued behind them (refit.scale 58-87 ms -> 14-45 ms per cycle, the result the same to the bit). - Refinement: the main context and every trial slot fit on their pool (robust); the CPU engine keeps FitModelScale's robust fit. The card's fit is FitModelScale's to rounding (float residuals and weights, the steps' anisotropic factor in float), deterministic run to run and whichever engine a fit takes: R-work/R-free of the 14 parity sets on the GPU equal the CPU engine's to 4 digits (largest difference 1.4e-6 on the 3.6 A set). Test: ModelScaleGPUPool_RobustMatchesFitModelScale (one reflection in fifty a 4x outlier, five cells). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu (cherry picked from commit d15f048cbd7ed2d6b430f9f9cb5018ca284da8f0)	2026-10-26 01:38:24 +01:00
leonarski_f	8261b7354e	v1.0.0-rc.176 (#86) ... * jfjoch_broker: Optional live Rugnux analysis of DECTRIS rotation collections inside the broker, with merged data, maps and the placed model written beside the master file, the rotation lattice in the scan result, and a "Rugnux live" tab and dashboard row in the web frontend. * Rugnux: Faster processing, with molecular replacement and model validation spread over several GPUs and run on the Apple GPU on macOS. * Rugnux: The polarization stated in miniCBF and d*TREK headers is used, twinned crystals' space groups are decided more reliably, and a run that finds no lattice still reports every image. Reviewed-on: #86 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>	2026-10-19 07:03:28 +02:00
leonarski_fandFilip Leonarski	19b135186d	v1.0.0-rc.175 (#85) ... * Rugnux: Processes a collection live while jfjoch_broker writes it (`rugnux <master.h5> <broker URL>`). * Rugnux: Processing starts on the first images and confirms its choices once all are in, so analysis can begin during collection. * Rugnux: Faster processing, especially on servers with several GPUs and CPU sockets and on computers without a GPU (`--no-gpu`, macOS). * Rugnux: Uses much less GPU memory, so large data sets stay on the GPU of smaller cards, and a data set too large for the card is processed on the CPU instead. * Rugnux: On macOS, Bragg integration runs on the Apple GPU. * Rugnux: Better handling of twinning and pseudo-symmetry, with the twin hypothesis written as `<prefix>_twin_<SG>.mtz`, and a wrong `--model` no longer confirms the data or decides the hand. * Rugnux: `--model` accepts predicted (AlphaFold) models and falls back to molecular replacement when the model is not placed as a rigid body (experimental `--mr` forces it), settling space group, hand and twinning and writing a different answer as `<prefix>_mr.mtz`. * Rugnux: Molecular replacement is faster and settles the space group in more cases with several copies in the asymmetric unit. * Rugnux: Deposition-ready mmCIF, a richer report, and numbered steps on the terminal with the full log in `<prefix>_log.txt`; `_P1.mtz`, `--estimate-beam-center` and `--beam-center-search` are removed. * Rugnux: The beam-stop shadow is found correctly on tilted detectors. * Rugnux: Stills files that record a stationary goniometer axis get the default stills geometry refinement. * jfjoch_viewer: "Analyze dataset" uses the same defaults as a bare `rugnux <file>`, and shows the results of processed CBF, marCCD and SMV sweeps. * jfjoch_viewer: The Files panel refreshes on F5 and every few seconds, and the plot type and other selections stay put during live collection. * jfjoch_writer: Memory stays bounded and old data files are closed efficiently on long runs, any failure ends the run in an error state the broker is told about, and the master file appears only after its data files; the ZeroMQ pusher logs when a stalled writer makes it drop images. * Fixes: The TCP image pusher no longer blocks the broker's receiver, GPU out-of-memory failures stop with a clear message, and the XDS plugin masks module and chip edges. --------- Co-authored-by: Filip Leonarski <jungfrau@filips-mac-mini.home> Reviewed-on: #85 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>	2026-10-16 15:34:43 +02:00