{
  "abstract": "Monte Carlo particle transport enables high-fidelity astrophysical radiation and neutrino simulations - from core-collapse supernovae and neutron-star mergers to accretion flows - by handling multidimensional geometries, frequency dependence, and moving media without angular discretization. However, inter-rank communication limits scalability on unstructured meshes: standard two-sided MPI requires receivers to post receives and poll completions, creating per-iteration progress overhead that grows with the number of communication partners. Such problems have not demonstrated high scaling efficiency at $O(10^4)$ cores. We present STORM (Scalable Transport via One-sided Remote Memory), an open-source library for Monte Carlo transport on general meshes, physics, and boundary conditions. STORM provides a lock-free, mesh-independent communication layer that replaces MPI's matched-send/receive semantics with Remote Direct Memory Access (RDMA) - one-sided operations that write directly into a remote rank's memory without involving its CPU. Each rank pair shares a single-producer, single-consumer ring buffer; RDMA writes transfer particles while receivers remain passive. A two-sided MPI backend provides a portable fallback. In an adversarial uniform-emission benchmark, the RDMA backend sustains $>97\\%$ weak-scaling and $>88\\%$ strong-scaling efficiency up to 13,440~cores (112~cores per network adapter), with $1.14$-$1.27\\times$ speedups over the two-sided alternative. In a Hohlraum IMC benchmark at 4480 ranks, it is $1.41\\times$ faster because MPI progress overhead is reduced by $6.1\\times$. By decoupling communication from physics models and mesh representations, STORM removes a barrier to scaling Monte Carlo transport in astrophysical multiphysics codes, enabling coupled radiation-hydrodynamics with energy- and angle-resolved photon or neutrino transport on dynamically evolving meshes at scale.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: ApJS 286, 67 (2026)",
  "arxiv": "2607.20639",
  "author_total": 3,
  "authors": [
    {
      "name": "Maor Mizrachi"
    },
    {
      "name": "Barak Raveh"
    },
    {
      "name": "Elad Steinberg"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/anqp0d6rpmbvc.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.20639",
  "fulltext_url": null,
  "id": "anqp0d6rpmbvc",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.20639",
  "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.IM"
    ],
    [
      "arxiv",
      "cs.DC"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2607.20639",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/anqp0d6rpmbvc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:52Z",
      "native_id": "2607.20639",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.20639"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    }
  ],
  "title": "STORM: RDMA-based Monte Carlo Transport Scheme for Distributed-Memory Particle Simulations",
  "title_text": "STORM: RDMA-based Monte Carlo Transport Scheme for Distributed-Memory Particle Simulations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:41Z",
  "url": "https://research.draconitic.com/paper/anqp0d6rpmbvc",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.3847/1538-4365/aea3f8",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}