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Jungfraujoch/image_analysis/structure_refinement/ModelScaleGPU.h
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leonarski_fandFilip Leonarski 19b135186d
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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

95 lines
4.1 KiB
C++

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#pragma once
#include <array>
#include <cstddef>
#include <vector>
#include <cuda_runtime.h>
#include "../indexing/CUDAMemHelpers.h"
// The rigid body's scale fit on the GPU: what RigidBodyTarget::Residuals does with a gemmi
// Scaling<float> (use_solvent, k_sol and b_sol fixed) - fit_isotropic_b_approximately() followed by
// gemmi's Levenberg-Marquardt, and FitModelScale's k_sol/b_sol grid. The sums over the reflections run
// on the device, accumulated in double in a fixed order; the Levenberg-Marquardt control (the damping,
// the 7x7 solve, the stop rules) is gemmi's own, ported line for line and run on the host.
struct ModelScaleParams {
double k_overall = 1.0;
double b_star[6] = {0, 0, 0, 0, 0, 0}; // gemmi SMat33 order u11 u22 u33 u12 u13 u23
};
struct ModelSolventFit {
double k_sol = 0.35, b_sol = 46.0; // gemmi's Scaling defaults when the fit does not run
double r = 1.0; // ModelScaleReport::r_work_fit
int n_grid = 0;
ModelScaleParams scale; // the winner's k_overall and b_star
};
// One fit's parameters for one launch, and which sums to take over the points.
struct ModelScaleFitState {
double k_overall;
double b_star[6];
double k_sol, b_sol;
int mode;
int column; // the fit's row of |Fcalc + solvent| on the device
};
class ModelScaleGPU {
public:
static size_t DeviceBytes(size_t max_points);
ModelScaleGPU(cudaStream_t stream, size_t max_points);
// Per zone: the Scaling points in gemmi prepare_points() order, adp_symmetry_constraints(sg) rows
// and the cell's fractionalization matrix (UnitCell::frac.mat, row-major). n <= max_points.
void SetPoints(const std::vector<std::array<int, 3>> &hkl,
const std::vector<double> &stol2,
const std::vector<float> &fobs,
const std::vector<float> &sigma,
const std::vector<std::array<double, 6>> &constraints,
const double frac[9]);
// fit_isotropic_b_approximately() + fit_parameters() at a fixed solvent pair, from k_overall = 1 and
// b_star = 0. d_fcmol, d_fmask: one float2 per point on the device.
ModelScaleParams Fit(const float2 *d_fcmol, const float2 *d_fmask, double k_sol, double b_sol);
// FitModelScale (ModelScaling.cpp) with the default box, b_sol up to b_hi: the same grid, the same
// winner. Throws ModelScaleGPUTooFewReflections where FitModelScale would chain the grid points
// (five or fewer reflections for the isotropic fit).
ModelSolventFit FitSolvent(const float2 *d_fcmol, const float2 *d_fmask, double b_hi = 80.0);
private:
struct LevMarRun;
// Queues the copy of `fits` to the device. host_fits_ is written only after the stream has been
// synchronized, which every Reduce() ends with.
void UploadFits(const std::vector<ModelScaleFitState> &fits);
// The sums of the first `nfits` uploaded fits over all points, one row of SLOTS doubles per fit, on
// the host.
const double *Reduce(int nfits);
const double *Sums(const std::vector<ModelScaleFitState> &fits);
void FitBatch(const float2 *d_fcmol, const float2 *d_fmask, std::vector<ModelScaleFitState> &fits);
cudaStream_t stream_;
size_t max_points_;
int n_ = 0;
int n_strong_ = 0; // points fit_isotropic_b_approximately() fits on
int n_params_ = 1; // k_overall + one per constraint row
double constraints_[6][6] = {};
double frac_[9] = {};
CudaDevicePtr<int> hkl_; // 3 per point
CudaDevicePtr<double> stol2_;
CudaDevicePtr<float> fobs_;
CudaDevicePtr<unsigned char> strong_;
CudaDevicePtr<float> f_abs_; // per fit of a batch, per point
CudaDevicePtr<ModelScaleFitState> fits_;
CudaDevicePtr<double> partial_; // per fit, per block, per slot
CudaDevicePtr<double> sums_; // per fit, per slot
CudaHostPtr<ModelScaleFitState> host_fits_;
CudaHostPtr<double> host_sums_;
};