{
  "abstract": "We present the GPU implementation of the general-purpose interior-point solver Clarabel for convex optimization problems with conic constraints. We introduce a mixed parallel computing strategy that processes linear constraints first and then handles other conic constraints in parallel. The GPU solver currently supports linear equality and inequality constraints, second-order cones, exponential cones, power cones, and positive semidefinite cones of the same dimensionality. We demonstrate that integrating a mixed parallel computing strategy with GPU-based direct linear system solvers enhances the performance of GPU-based conic solvers, surpassing their CPU-based counterparts across a wide range of conic optimization problems. We also show that employing mixed-precision linear system solvers can potentially achieve additional acceleration without compromising solution accuracy.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Engineering Science, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland and School of Engineering, École Polytechnique Fédérale De Lausanne, Lausanne, Switzerland",
    "Department of Computer Science, Stanford University, Stanford, California, USA",
    "Department of Electrical Engineering, Stanford University, Stanford, California, USA",
    "Department of Engineering Science, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yuwen Chen",
      "orcid": "0000-0003-3129-7698"
    },
    {
      "affs": [
        1
      ],
      "name": "Danny Tse",
      "orcid": "0009-0004-5277-0346"
    },
    {
      "affs": [
        2
      ],
      "name": "Parth Nobel",
      "orcid": "0000-0002-8228-7441"
    },
    {
      "affs": [
        3
      ],
      "name": "Paul J. Goulart",
      "orcid": "0000-0002-0456-4124"
    },
    {
      "affs": [
        2
      ],
      "name": "Stephen Boyd",
      "orcid": "0000-0001-8353-6000"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6nrs4v7d7cf30.bib",
  "cited_by": 6,
  "doi": "10.1145/3815420",
  "fulltext_url": null,
  "id": "6nrs4v7d7cf30",
  "issns": [
    "0098-3500",
    "1557-7295"
  ],
  "landing_url": "https://doi.org/10.1145/3815420",
  "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": [
    [
      "openalex",
      "2612|Advanced Optimization Algorithms Research"
    ]
  ],
  "openalex": "W4405901344",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-21",
  "publisher": "Association for Computing Machinery",
  "ris": "https://research.draconitic.com/paper/6nrs4v7d7cf30.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T13:21:00Z",
      "native_id": "W4405901344",
      "source": "openalex",
      "url": "https://doi.org/10.1145/3815420"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2612|Advanced Optimization Algorithms Research",
      "subfield": "numerical-analysis"
    }
  ],
  "title": "CuClarabel: GPU Acceleration for a Conic Optimization Solver",
  "title_text": "CuClarabel: GPU Acceleration for a Conic Optimization Solver",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T13:20:22Z",
  "url": "https://research.draconitic.com/paper/6nrs4v7d7cf30",
  "venue": "ACM Transactions on Mathematical Software",
  "versions": []
}