AI 中文总结
Claude-SD 是兼容 mumax3 脚本的跨平台 CPU/GPU 微磁模拟器，兼具双精度支持与高效性，填补了非 NVIDIA 硬件研究人员的使用空白，吞吐量与精度表现优异。
AI 中文摘要
定量自旋电子学日益依赖少数 GPU 微磁代码，但这些代码均要求 NVIDIA 硬件且全程使用单精度运算，导致无此类硬件的研究人员无法运行甚至标准验证问题。本文报告 Claude-SpinDynamics（Claude-SD），一款全新开源微磁模拟器，其核心为跨平台 C++20（支持 Windows 和 Linux）并配备 Python 接口，填补了这一空白：完整 CPU 版本通过与 GPU 路径相同的测试套件验证，无需任何加速器即可运行所有单元测试和 uMAG 标准问题；GPU 版本则提供单精度和双精度两种精度选择，可选两种退磁 FFT 后端，原生实现了自旋轨道、自旋转移、Zhang-Li 扭矩、Dzyaloshinskii-Moriya 相互作用及每单元项。Claude-SD 原生解析 mumax3 的 .mx3 脚本语言，现有社区脚本可直接运行；在匹配条件下，两款代码逐单元结果一致至单精度舍入，与 mumax+ 和 OOMMF 在 uMAG 动态切换标准问题上的差异在 2% 以内，MuMax-CO 在同一问题上与 mumax3 结果一致至 float32 舍入。与这三款代码直接基准测试相比，Claude-SD 的单精度版本在小型和二维问题上是最快求解器，在最大网格尺寸下仍具竞争力；其双精度和双 FFT 后端路径在 GPU 微磁代码中无出其右。GPU 复制批处理扩展进一步在每步单个内核启动中推进整个有限温度轨迹集合，相比逐试验循环吞吐量提升 1 至 2 个数量级，同时将单轨迹结果复现至数值舍入级别。完整源代码采用开放许可证分发，附带可运行示例笔记本和文档以供独立复现。
英文摘要
Quantitative spintronics increasingly depends on a handful of GPU micromagnetic codes, all of which require NVIDIA hardware and single precision throughout, leaving researchers without such hardware unable to run even the standard validation problems. We report Claude-SpinDynamics (Claude-SD), a new open source micromagnetic simulator with a cross platform C++20 core (Windows and Linux) and a Python interface that closes this gap: a complete CPU build, validated by the same test suite as the GPU path, runs every unit test and uMAG standard problem with no accelerator at all, alongside GPU builds offering both single and double precision, a choice of two demagnetization FFT backends, and natively implemented spin-orbit, spin-transfer, and Zhang-Li torques, Dzyaloshinskii-Moriya interaction, and percell materials.Claude-SD natively interprets mumax3's .mx3 scripting language, so existing community scripts run unmodified; under matched conditions the two codes agree cell-by cell to single-precision round-off, and, together with mumax+ and OOMMF, to within 2% on the uMAG dynamic-switching standard problem, with MuMax-CO agreeing to mumax3 to float32 round-off on the same problem. Benchmarked head-to-head against these three codes, Claude-SD's single-precision build is the fastest solver on small and two-dimensional problems and remains competitive at the largest grid sizes, while its double-precision and dual-FFT-backend paths are unmatched among GPU micromagnetic codes. A GPU replicabatching extension further advances an entire ensemble of finite-temperature trajectories in a single kernel launch per step, giving one to two orders of magnitude of throughput over a per-trial loop while reproducing single-trajectory results to numerical round-off. The complete source is openly licensed and distributed with runnable example notebooks and documentation for independent reproduction.
Comments23 pages, 12 figures
