{
  "abstract": "Hybrid particle-in-cell simulations are widely used to study kinetic processes in collisionless astrophysical and space plasmas, yet the high computational cost of large-scale three-dimensional runs has largely confined production studies to two dimensions or restricted domains. To address this challenge, we present a performance-portable GPU implementation of the hybrid particle-in-cell code dHybridR. The implementation combines OpenMP target offloading with specialized SYCL kernels for the most computationally expensive operations, while preserving a unified CPU-GPU codebase that supports Intel, AMD, and NVIDIA GPUs. On the exascale supercomputers Aurora and Frontier, dHybridR achieves weak scaling efficiencies of $86\\%$ to $97\\%$ across 49,152 accelerators, and at 256 particles per cell, its full-node GPU throughput is up to $32\\times$ that of the vectorized CPU implementation at approximately $90\\%$ less energy per particle-update. To our knowledge, no other hybrid particle-in-cell code has reported GPU performance at this scale, leaving dHybridR uniquely positioned to exploit exascale systems. These advances substantially lower the computational barrier to large-scale three-dimensional hybrid-kinetic simulations of collisionless plasmas.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.28422",
  "author_total": 3,
  "authors": [
    {
      "name": "Bricker Ostler"
    },
    {
      "name": "Miha Cernetic"
    },
    {
      "name": "Damiano Caprioli"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ef2z215xtk1c8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.28422",
  "fulltext_url": null,
  "id": "ef2z215xtk1c8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.28422",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.HE"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "arxiv",
      "physics.plasm-ph"
    ],
    [
      "openalex",
      "1708|Parallel Computing and Optimization Techniques"
    ]
  ],
  "openalex": "W7214186307",
  "pdf_url": "https://arxiv.org/pdf/2609.28422",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ef2z215xtk1c8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:56Z",
      "native_id": "2609.28422",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.28422"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "Performance-portable GPU acceleration of the hybrid particle-in-cell code dHybridR",
  "title_text": "Performance-portable GPU acceleration of the hybrid particle-in-cell code dHybridR",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:56Z",
  "url": "https://research.draconitic.com/paper/ef2z215xtk1c8",
  "venue": "arXiv",
  "versions": []
}