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* Rugnux: Rotation processing recovers most of the speed lost since 1.0.0-rc.180; live processing in jfjoch_broker finishes sooner. * Rugnux: More reliable rotation processing and indexing, including small molecules and supercells. * Rugnux: More robust serial data and model refinement; molecular replacement handles multi-chain models. * jfjoch_broker (experimental): Live serial processing keeps the detector geometry fixed for the whole run. * jfjoch_broker: Pixel sum without an FPGA, a mask bit for inactive pixels, and several stability fixes. * jfjoch_broker: Rotation indexing and quick integration moved to rugnux live mode; their API settings are deprecated and ignored. * jfjoch_broker, Rugnux: Azimuthal integration can use logarithmic (log10) Q bins or a given number of bins, set from the API, the web interface and the Rugnux command line. * jfjoch_viewer: Build and display fixes; the README is rewritten. Reviewed-on: #95 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
116 lines
5.8 KiB
C++
116 lines
5.8 KiB
C++
// SPDX-FileCopyrightText: 2026 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 <array>
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#include <cstddef>
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#include <vector>
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#include <cuda_runtime.h>
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#include "../indexing/CUDAMemHelpers.h"
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// The rigid body's scale fit on the GPU: what RigidBodyTarget::Residuals does with a gemmi
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// Scaling<float> (use_solvent, k_sol and b_sol fixed) - fit_isotropic_b_approximately() followed by
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// gemmi's Levenberg-Marquardt, and FitModelScale's k_sol/b_sol grid. The sums over the reflections run
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// on the device, accumulated in double in a fixed order; the Levenberg-Marquardt control (the damping,
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// the 7x7 solve, the stop rules) is gemmi's own, ported line for line and run on the host.
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struct ModelScaleParams {
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double k_overall = 1.0;
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double b_star[6] = {0, 0, 0, 0, 0, 0}; // gemmi SMat33 order u11 u22 u33 u12 u13 u23
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};
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struct ModelSolventFit {
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double k_sol = 0.35, b_sol = 46.0; // gemmi's Scaling defaults when the fit does not run
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double r = 1.0; // ModelScaleReport::r_work_fit
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int n_grid = 0;
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ModelScaleParams scale; // the winner's k_overall and b_star
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};
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// One fit's parameters for one launch, and which sums to take over the points.
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struct ModelScaleFitState {
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double k_overall;
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double b_star[6];
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double k_sol, b_sol;
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int mode;
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int column; // the fit's row of |Fcalc + solvent| on the device
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int weighted; // the robust fit's weights (row `column`) multiply the WSSR and J^T J, J^T r; 0: weight 1
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double c; // the robust fit's scale of the residuals (ModelScaling.cpp, RobustReweight)
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};
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class ModelScaleGPU {
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public:
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static size_t DeviceBytes(size_t max_points);
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// The module of its kernels loaded on the current device now, not at their first launch (lazy loading).
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static void LoadModule();
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ModelScaleGPU(cudaStream_t stream, size_t max_points);
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// Per zone: the Scaling points in gemmi prepare_points() order, adp_symmetry_constraints(sg) rows
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// and the cell's fractionalization matrix (UnitCell::frac.mat, row-major). n <= max_points.
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void SetPoints(const std::vector<std::array<int, 3>> &hkl,
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const std::vector<double> &stol2,
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const std::vector<float> &fobs,
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const std::vector<float> &sigma,
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const std::vector<std::array<double, 6>> &constraints,
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const double frac[9]);
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// fit_isotropic_b_approximately() + fit_parameters() at a fixed solvent pair, from k_overall = 1 and
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// b_star = 0. d_fcmol, d_fmask: one float2 per point on the device.
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ModelScaleParams Fit(const float2 *d_fcmol, const float2 *d_fmask, double k_sol, double b_sol);
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// FitModelScale (ModelScaling.cpp) with the default box, b_sol up to b_hi: the same grid, the same
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// winner. Throws ModelScaleGPUTooFewReflections where FitModelScale would chain the grid points
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// (five or fewer reflections for the isotropic fit).
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// robust: the fine pass by robust least squares, as FitModelScale with ModelScaleBox::robust - the
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// reweighting on the device, the median of each fit's first residuals included.
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// k_start, b_start: ModelScaleBox's - the fine pass around them, no coarse pass (negative: as FitModelScale).
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ModelSolventFit FitSolvent(const float2 *d_fcmol, const float2 *d_fmask, double b_hi = 80.0,
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bool robust = false, double k_start = -1, double b_start = -1);
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// What a robust FitSolvent takes on the card beyond DeviceBytes(), reserved at its first call.
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static size_t RobustBytes(size_t max_points);
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private:
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struct LevMarRun;
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// Queues the copy of `fits` to the device. host_fits_ is written only after the stream has been
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// synchronized, which every Reduce() ends with.
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void UploadFits(const std::vector<ModelScaleFitState> &fits);
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// The sums of the first `nfits` uploaded fits over all points, one row of SLOTS doubles per fit, on
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// the host.
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const double *Reduce(int nfits);
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// The stream's queued work done, polled for: a fit's launches are short and the host waits for every one
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// of them, so sleeping until the device raises the wake-up (set_gpu_blocking_sync) would cost more than
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// the work itself.
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void Wait();
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const double *Sums(const std::vector<ModelScaleFitState> &fits);
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void FitBatch(const float2 *d_fcmol, const float2 *d_fmask, std::vector<ModelScaleFitState> &fits);
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// gemmi's Levenberg-Marquardt for every fit in `fits` from the parameters it holds.
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void LevMarBatch(std::vector<ModelScaleFitState> &fits);
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// The robust rounds of a batch FitBatch() has fitted: reweight, fit again, ROBUST_ROUNDS times.
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void RobustBatch(std::vector<ModelScaleFitState> &fits);
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cudaStream_t stream_;
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size_t max_points_;
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int n_ = 0;
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int n_strong_ = 0; // points fit_isotropic_b_approximately() fits on
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int n_params_ = 1; // k_overall + one per constraint row
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double constraints_[6][6] = {};
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double frac_[9] = {};
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CudaDevicePtr<int> hkl_; // 3 per point
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CudaDevicePtr<double> stol2_;
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CudaDevicePtr<float> fobs_;
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CudaDevicePtr<unsigned char> strong_;
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CudaDevicePtr<float> f_abs_; // per fit of a batch, per point
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CudaDevicePtr<ModelScaleFitState> fits_;
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CudaDevicePtr<double> partial_; // per fit, per block, per slot
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CudaDevicePtr<double> sums_; // per fit, per slot
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CudaHostPtr<ModelScaleFitState> host_fits_;
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CudaHostPtr<double> host_sums_;
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CudaDevicePtr<float> weight_; // the robust fit's residuals, then its weights in their place, per fit of a batch, per point
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CudaDevicePtr<double> median_; // the robust fit's c, per fit of a batch
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CudaHostPtr<double> host_median_;
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};
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