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LKlight-GPU v1.2.0 — 0.5 Å far-field grid + batch-cache fix (GPU ~66× faster long runs)

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@LK-Studio1128 LK-Studio1128 released this 05 Sep 17:44
· 21 commits to main since this release
v1.2.0
7d64526

LKlight-GPU v1.2.0

0.5 Å far-field grid (reference-level accuracy) + GPU batch-scoring
performance optimisations + faster grid setup.

Added

  • Far-field grid at 0.5 Å reference resolution (new default). The host-side
    far-field grid (src/grid_dna.rs, byte-identical with LKlight-grid) is built
    at 0.5 Å spacing; the box half-width is spread = ceil(FIELD_RMAX / spacing) + 2
    (the old hard-coded ±32 cells assumed 1 Å). Because the CUDA far-field kernels
    are fully parameterised by the grid (spacing / origin / nx,ny,nz / phi) with no
    spacing assumption baked into device code, the GPU shares the accuracy gain
    with no kernel change. Verified on Linux RTX 3080 Ti and Windows RTX 5090:
    worst-case per-pose deviation vs. the reference engine drops ~9.6 → ~6.9
    (~1.4×).

Changed

  • Per-block shared-memory reduction of pose atomics (full_score.cu):
    each thread block reduces per-atom electrostatic/VDW terms into extern __shared__ memory and performs one atomicAdd per block via a single thread
    (stride-halving tree reduction), instead of every ligand atom doing a double
    atomicAdd. Per-pose atomic traffic drops from ~nl to ~ceil(nl/256).
    Out-of-range threads store 0 and still hit the barrier. GSO convergence is
    unchanged (1AZP dna best −7254.95161 GPU vs −7254.95220 optimised CPU-grid,
    Δ ≈ 5.9×10⁻⁴, within the f32 batch budget).
  • Persistent device-buffer cache (full_score.cu): cuda_batch_score keeps
    a process-static BatchCache holding resident device copies of the receptor
    field, constant tables and grid parameters, keyed by host pointers + dims. On
    a hit only the N×7 pose translations/rotations are uploaded and N×2 energies
    read back each call; a new receptor (pointer-key change) frees + rebuilds.
    Eliminates repeated field upload across thousands of GSO scoring calls.
  • Shell-band box scan for faster grid setup (bit-identical). Same shared
    grid_dna.rs pruning as LKlight-grid: written-cell set/order and the f32
    field are unchanged; grid build + first score 1.57 s → 1.02 s at 0.5 Å.
  • Ligand C-cache pointer stabilisation (performance + robustness fix).
    batch_energy_gpu_scores rebuilt the ligand parameter vectors on every call,
    so the C-side batch-cache key (base-pointer) never matched and every step did
    a full rebuild (repeated cudaMalloc/cudaFree + full 0.5 Å field upload).
    Ligand arrays are now cached once per system (DNA.lig_cuda, OnceLock,
    cfg(cuda)) with stable pointers, so the persistent device cache actually
    hits step-to-step. Also clears any sticky CUDA error at the batch entry so a
    single failed step can no longer poison later launches, and reports numeric
    error codes in diagnostics. Measured on Windows RTX 5090, 1000 glowworms ×
    1000 steps, 1AZP dna: GPU wall-clock 267.6 s → 4.05 s (~66×), batch
    errors → 0, best energy unchanged (−7254.9516108, bit-identical).

Accuracy contract

  • pydock / cpydock GPU == CPU bit-identical; dna differs only by the
    expected f32 near-field batch rounding (≤ ~6×10⁻⁴).

Downloads

Platform File Binary
Linux x86-64 LKlight-linux-cuda CUDA-enabled, glibc
Windows x64 LKlight-win64-cuda.exe CUDA-enabled, PE32+ console

Static cudart: the target machine only needs the NVIDIA driver. Bare binaries
follow the v1.1.0 asset layout; each repo tree carries README.md, LICENSE
(GPL-3.0), NOTICE and CHANGELOG.md.

Citation & DOI

Zenodo DOI from v1.1.0 is retained. See NOTICE for LightDock attribution.

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