Files
leonarski_fandClaude Opus 5.5 c286e47583 Refinement: its GPU modules loaded in the model warm-up, not at Step 11
The intermittent 1.1-2.7 s stall at the start of live refinement (TIMING
refine.engine) is the CUDA driver's lazy module load. Stack samples of the broker in
a stalled run show the engine's first kernel launch (phase_table_kernel in
RefinementGPUCard) inside cuLibraryLoadData for the whole stall, and per-thread
/proc samples show the process using no CPU meanwhile: it is waiting for the
executable's pages. On this machine the binaries are on a busy, nearly full HDD and
the page cache is under pressure, so pages of the executable touched for the first
time late in a run - the refinement's fatbin, and in the worst runs other code and
data too (refine.topology 0.015 -> 2.5 s, refine.placement 0.15 -> 2.3 s) - can take
seconds to come back. Reproduced on purpose: dropping the broker's unmapped pages
from the page cache with a 4 GB write to /data beside it gives refine.engine
0.45 / 0.85 s (0.02 s normally).

WarmUpModelValidation, which the broker runs when a model is uploaded, now also loads
the modules of the refinement engine's and the scale fit's kernels (the two
refinement TUs a validation does not launch) on every card. With that, the same
induced load gives refine.engine 0.020 / 0.020 / 0.021 s. Other cold pages can still
stall under such load; keeping the binaries on a local SSD avoids all of it.

Live thau replays, no load added: refine.engine 0.020 s in 3 of 3, refinement 2.9 s
(rc184-port 3.1-3.2 s, with the two previous levers in). The warm-up only loads
modules, so the results are unchanged (same R-work/R-free and evaluations live;
ModelValidation_WarmUpChangesNothing passes).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HVeDQsU5a8AJUdJPgWevAK
2026-11-08 22:57:41 +01:00

116 lines
5.8 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
int weighted; // the robust fit's weights (row `column`) multiply the WSSR and J^T J, J^T r; 0: weight 1
double c; // the robust fit's scale of the residuals (ModelScaling.cpp, RobustReweight)
};
class ModelScaleGPU {
public:
static size_t DeviceBytes(size_t max_points);
// The module of its kernels loaded on the current device now, not at their first launch (lazy loading).
static void LoadModule();
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).
// robust: the fine pass by robust least squares, as FitModelScale with ModelScaleBox::robust - the
// reweighting on the device, the median of each fit's first residuals included.
// k_start, b_start: ModelScaleBox's - the fine pass around them, no coarse pass (negative: as FitModelScale).
ModelSolventFit FitSolvent(const float2 *d_fcmol, const float2 *d_fmask, double b_hi = 80.0,
bool robust = false, double k_start = -1, double b_start = -1);
// What a robust FitSolvent takes on the card beyond DeviceBytes(), reserved at its first call.
static size_t RobustBytes(size_t max_points);
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);
// The stream's queued work done, polled for: a fit's launches are short and the host waits for every one
// of them, so sleeping until the device raises the wake-up (set_gpu_blocking_sync) would cost more than
// the work itself.
void Wait();
const double *Sums(const std::vector<ModelScaleFitState> &fits);
void FitBatch(const float2 *d_fcmol, const float2 *d_fmask, std::vector<ModelScaleFitState> &fits);
// gemmi's Levenberg-Marquardt for every fit in `fits` from the parameters it holds.
void LevMarBatch(std::vector<ModelScaleFitState> &fits);
// The robust rounds of a batch FitBatch() has fitted: reweight, fit again, ROBUST_ROUNDS times.
void RobustBatch(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_;
CudaDevicePtr<float> weight_; // the robust fit's residuals, then its weights in their place, per fit of a batch, per point
CudaDevicePtr<double> median_; // the robust fit's c, per fit of a batch
CudaHostPtr<double> host_median_;
};