{
  "abstract": "Materials exhibiting noncollinear magnetism or strong spin-orbit-coupling underpin many spintronic and topological applications, but their simulations require complex two-component spinors and costs substantially more than scalar density functional theory (DFT). We present a GPU-centric exascale finite-element DFT framework with noncollinear magnetism and spin-orbit-coupling, combining (i) adaptive higher-order finite-element discretization, (ii) a matrix-free Poisson solver, (iii) residual-based Chebyshev filtered subspace iteration (R-ChFSI) for sparse generalized eigenproblems, (iv) R-ChFSI-enabled mixed precision computation with block floating-point compressed MPI communication, and (v) communication-efficient band partitioning. R-ChFSI permits inexact matrix-multivector products and $4\\times$--$6.4\\times$ compression of communicated data relative to FP64 while preserving double-precision robustness and reducing computation and data movement. Results demonstrate strong scaling on Aurora and Frontier, with up to $3.4\\times$ faster Chebyshev filtering. On Aurora, total self-consistent-field (SCF) solve wall time reduces by $2\\times$. An 800-node (9,600-GPU) calculation demonstrates fully relativistic pseudopotential DFT for $\\sim$80,000 electrons in under 7 minutes per SCF iteration.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.30128",
  "author_total": 9,
  "authors": [
    {
      "name": "Nikhil Kodali"
    },
    {
      "name": "Gourab Panigrahi"
    },
    {
      "name": "Nishant Gupta"
    },
    {
      "name": "Kartick Ramakrishnan"
    },
    {
      "name": "Sundaresan G"
    },
    {
      "name": "Rudra Panch"
    },
    {
      "name": "Sambit Das"
    },
    {
      "name": "Vishwas Rao"
    },
    {
      "name": "Phani Motamarri"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/47yxppswe8aan.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.30128",
  "fulltext_url": null,
  "id": "47yxppswe8aan",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.30128",
  "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",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "1708|Parallel Computing and Optimization Techniques"
    ]
  ],
  "openalex": "W7162811645",
  "pdf_url": "https://arxiv.org/pdf/2605.30128",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/47yxppswe8aan.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:26Z",
      "native_id": "2605.30128",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.30128"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Towards exascale fully relativistic pseudopotential density functional theory calculations enabled by mixed-precision computation and compressed-communication using residual based subspace iteration",
  "title_text": "Towards exascale fully relativistic pseudopotential density functional theory calculations enabled by mixed-precision computation and compressed-communication using residual based subspace iteration",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:26Z",
  "url": "https://research.draconitic.com/paper/47yxppswe8aan",
  "venue": "arXiv",
  "versions": []
}