{
  "abstract": "Micromagnetic simulations are essential tools in nanomagnetism and spintronics research. Although widely adopted solvers like Mumax3 and the Python-native magnum.np use GPU acceleration to improve performance, these tools are limited to single-device computation. In this work, we present, to our knowledge, the first Python-native multi-GPU micromagnetic framework by extending magnum.np with PyTorch Distributed. This leverages high-speed communication and computation across multiple GPUs while retaining the benefits of ease of installation, platform-agnostic design, and compatibility with Python. For computationally intensive demagnetisation effective-field calculations, we achieve a 7.0x speedup on a synthetic benchmark with 42.8M cells across 8 GPUs connected via NVLink, whereas the Halo exchange required for Heisenberg exchange shows limited scalability due to kernel dispatch latency. We also demonstrated the framework’s versatility by achieving a 6.8x speedup in demagnetisation field computation on the CPU with NUMA pinning via the MPI backend of PyTorch Distributed, using a 1M-cell synthetic benchmark. Faster turnaround times will enable researchers to explore larger, more complex systems and accelerate the design cycle for novel spintronic devices. Source code: https://gitlab.com/jedcheng/magnum-np-distributed .",
  "abstract_shown": true,
  "affiliations": [
    "Graduate School of Information Science and Electrical Engineering, Kyushu University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tsz Chung Cheng"
    },
    {
      "affs": [
        0
      ],
      "name": "Yuichiro Kurokawa"
    },
    {
      "affs": [
        0
      ],
      "name": "Hiromi Yuasa"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3ptsg942acrvj.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-62507-1",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-62507-1",
  "id": "3ptsg942acrvj",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-62507-1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1708|Parallel Computing and Optimization Techniques"
    ]
  ],
  "openalex": "W7170137083",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62507-1_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/3ptsg942acrvj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:53Z",
      "native_id": "3e28ba7d889b4904afff757b2340cabf",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-62507-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "magnum.np.distributed: accelerating finite difference micromagnetic simulations with multiple GPUs",
  "title_text": "magnum.np.distributed: accelerating finite difference micromagnetic simulations with multiple GPUs",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:53Z",
  "url": "https://research.draconitic.com/paper/3ptsg942acrvj",
  "venue": "Scientific Reports",
  "versions": []
}