用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/ZhangXin8069/PyQCU --skill smear命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
PyQCU 性能基准 skill:覆盖 examples/benchmark 的通用基准,以及 dev87 strict MultiGrid 对 QUDA 的可复现公平计时与显存口径。
pyqcu.cuda 目录的完整生成 skill:C++ CUDA 后端(libqcu.so)的 Cython 桥接包;含 strict QUDA-style MultiGrid、params/argv/set_ptrs 参数协议与显存生命周期约束。
cpp/cuda/qcu/include 目录的完整生成 skill:26 个模板化 CUDA 头文件(内核内联),define.h 须镜像 pyqcu/cuda/define.py。
正在显示 SKILL.md
| name | smear |
| description | pyqcu.smear 目录的完整生成 skill:stout smearing(Morningstar-Peardon SU(3) 投影)与 Wuppertal 高斯模糊,含数值稳定性处理与 MPI 支持。 |
Gauge field smearing — spatial smoothing of gauge links to reduce UV noise.
| File | Purpose |
|---|---|
_stout.py | Stout smearing algorithm (copied/adapted from EasyDistillation's elemental generator) |
stout_smear(U, nstep=1, rho=0.12, support_parallel=False) → torch.TensorApply nstep iterations of stout smearing with parameter rho.
Algorithm (per step):
Numerical stability:
MPI support: When support_parallel=True, MPI boundary data (U_head, U_tail, U_head_tail) is recomputed each step since U changes with each smearing step.
The nstep>1 loop previously did not update U between steps — the loop variable was properly rebound but the MPI boundary data was computed outside the loop. Fixed by moving MPI exchange inside the step loop.
_wuppertal.py, 2026-08-24)与 stout 同目录的高斯模糊。bug33/34 修复要点(logs/fix-report-2026-08-24.md):
Gauge field: [3, 3, 4, Lx, Ly, Lz, Lt] = [color, color, direction, x, y, z, t]
Returned tensor has the same shape.
Has per-module force_use_npu flag. On NPU, the parity sign convention for f₀/f₁/f₂ uses explicit real/imag decomposition: