{
  "abstract": "This paper presents Chana, a GPU-accelerated, block-structured adaptive-mesh code for general relativistic magnetohydrodynamics, multigroup M1 neutrino transport, and dynamical spacetime evolution using the Z4c formulation. Each physical module uses independently selected spatial discretizations and ghost-zone widths to balance accuracy and computational cost. Stiff radiation-matter interactions are treated implicitly, with Jacobians calculated analytically from the chain-rules. The code is written in C++20 and it uses MPI and Kokkos 5 for distributed-memory and performance-portable parallelism. The code is validated through a number of GRMHD, radiation transport, and dynamic spacetime tests. For a representative core-collapse supernova configuration with three neutrino species and 12 energy groups per species, the code achieves approximately $1.1 \\times 10^6$ cell updates per GPU second on NVIDIA A100 GPUs using four-stage RK4 integration. Weak-scaling efficiency remains above 85\\% up to 256 GPUs on Perlmutter.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.07267",
  "author_total": 1,
  "authors": [
    {
      "name": "Tianshu Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/00vrwbgzb6dvb.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.07267",
  "fulltext_url": null,
  "id": "00vrwbgzb6dvb",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.07267",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.HE"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "1708|Parallel Computing and Optimization Techniques"
    ]
  ],
  "openalex": "W7220967564",
  "pdf_url": "https://arxiv.org/pdf/2610.07267",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/00vrwbgzb6dvb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:18Z",
      "native_id": "2610.07267",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.07267"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Chana: A GPU-Accelerated Adaptive-Mesh Code for General Relativistic Magnetohydrodynamics with Multigroup M1 Neutrino Transport",
  "title_text": "Chana: A GPU-Accelerated Adaptive-Mesh Code for General Relativistic Magnetohydrodynamics with Multigroup M1 Neutrino Transport",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:18Z",
  "url": "https://research.draconitic.com/paper/00vrwbgzb6dvb",
  "venue": "arXiv",
  "versions": []
}