leonarski_fandClaude Opus 5.5
d5fcf2f05b
ModelStructureFactorsGPU computes what compute_model_factors() and
map_from_coefficients() compute on the CPU - F_calc from the model's
density (IT92, Refmac-compatible blur, unblurred as prepare_asu_data()
does) and F_mask from the Refmac bulk-solvent mask, both on the
reflections prepare_asu_data(d_min) lists, in its order; and a map from
ASU coefficients on the grid get_size_for_hkl(coef, 0, 3.0) sizes - on a
device. Made once per cell, group, resolution and model, then evaluated
as often as the coordinates change, so refinement or MR can call it in a
loop. The device is an explicit parameter; every call leaves the calling
thread's current device as it found it.
Pieces:
- ModelDensityGPU: the rigid body's deterministic brick gather, moved
out of RigidBodyGPU.cu into a component of its own (ModelMaskGPU's
pattern); the rigid body uses it unchanged. MAX_BRICKS_PER_AXIS 8 ->
16, so fine grids with high-B atoms (lysozyme at 1.2 A, a 0.9 A P1
cell) are no longer refused; existing zones are gridded identically.
- One copy of the content is gridded and the symmetry composed in
reciprocal space (SymmetryComposition), operators applied on the fly;
the mask is ModelMaskGPU (every image of every atom, islands, shrink).
- Maps: gemmi's get_f_phi_on_grid() in ZYX order on the host (the
coefficients written are the same), in-place cuFFT c2r, transposed back
to XYZ on the device. One map at a time, in the engine's buffers.
Decided once, up front, per card, from its TOTAL memory: the engine's
bytes (16 N + cuFFT work + reflections, N the larger of the structure-
factor and map grids) must be at most half the card - the rigid body's
engines take at most a quarter beside it. Otherwise, or where the gather
cannot grid the cell, the CPU path runs, logged with needed vs total.
Anything to a resolution other than d_min (the null's 3.5 A fits) stays
on the CPU, so all replicates and the real model's side of the null are
computed the same way. A CUDA failure takes the existing path: the
validation restarts on the CPU.
Measured, model validation total per run (CPU path -> GPU), 16 GB card:
F432 215 A cubic, 1.30 A, 500^3 grid: 47.7 -> 15.6 s (two validations;
14.3 -> 2.4 and 33.4 -> 13.2, the rest of the second is writing the
three 0.5 GB maps); F_calc + F_mask 5.7 s -> 0.05 s
C2 1.11 A: 23.9 -> 13.6 s; P3_2 1.55 A: 18.2 -> 10.2 s;
P2_1 1.25 A: 13.4 -> 6.5 s; F4_132 328 A: 13.0 -> 5.5 s;
P6_5: 8.8 -> 4.2 s; P4_3 0.97 A: 4.6 -> 2.5 s; P1 0.92 A: 4.2 -> 2.2 s;
small P1: 3.2 -> 1.3 s; P6_1: 8.8 -> 6.2 s; lysozyme: 1.8 -> 1.4 s.
p.mtz md5-identical on all 13 sets. Against the CPU path: FC within
1e-4 of mean |F|, phases of the strong half within 0.003 deg, maps within
1e-4 (2mFo-DFc) and 7e-4 (mFo-DFc) of their rms; every logged R, CC,
FOM, k_sol and anomalous site list identical at the printed precision,
except where a rigid-body commit sat on an exact R-free tie (0.2155 ->
0.2155) and fell the other way (R-work 0.2127 vs 0.2129). The GPU result
is bit-identical run to run and with -N 8 (maps, map MTZ, placed model).
Peak device memory of the engine: 2.5 GB at 500^3 (process total peaked
at 14.4 GB with what the merge still holds).
Tests: ModelStructureFactorsGPU_MatchesCPU (five groups, 3.5 and 1.5 A:
same reflections, F_calc <= 1e-5 of mean |F|, F_mask 2e-7 rms, repeat
bit-identical), ModelStructureFactorsGPU_MapMatchesCPU (<= 5e-6 of rms).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SVmAWnzCmRKAXVUCdc4iNi
Jungfraujoch
Application to receive and analyse data from the PSI JUNGFRAU and EIGER detectors, together with
rugnux, its program for processing stored diffraction data - rotation and serial, macromolecular
and small-molecule - from images to merged reflections.
All documentation is now placed in docs/ subdirectory and for the current version hosted on Jungfraujoch Read The Docs page.