{
  "abstract": "GPU-based optimization faces two communication bottlenecks: coordinating frequent requests and delivering dense models that exceed device memory. We present a discrete simulated bifurcation (dSB) architecture that addresses both through NVIDIA DOCA GPUNetIO. For resident models, a persistent service receives field updates, executes each solve within one GPU thread block, and returns the result. Exact integer coupling sums, GPU work queues, and batched transmission keep the receive--solve--reply path on the device without a dedicated CPU data-path core. In comparisons with socket-based servers using the same solver, the largest latency gains occur under concurrent load. As the offered load increases from 400 to 800 thousand requests per second, median round-trip latency rises by only 6\\%. At the highest tested load, median and 99th-percentile latencies are 189 and 218~$μ$s, compared with 288 and 609~$μ$s for the tuned persistent CPU proxy across repeated runs. For models larger than device memory, a streaming solver retains dynamical state on the GPU and reuses incoming coupling tiles across replicas. It evaluates ten-million-variable dense binary matrices at approximately 307~Gb/s, consuming a 12.5-TB logical matrix through a 64-MiB packet buffer. Ground-state recovery on planted instances and agreement with reference executions verify the computation. Together, the two modes scale dSB to concurrent requests and dense models beyond GPU memory.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.06622",
  "author_total": 1,
  "authors": [
    {
      "name": "Yaocheng Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8w0vmwtjfesgf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.06622",
  "fulltext_url": null,
  "id": "8w0vmwtjfesgf",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.06622",
  "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",
      "cs.DC"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1708|Parallel Computing and Optimization Techniques"
    ]
  ],
  "openalex": "W7220742814",
  "pdf_url": "https://arxiv.org/pdf/2610.06622",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8w0vmwtjfesgf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2610.06622",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.06622"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "GPU-Initiated Discrete Simulated Bifurcation: Low-Latency Requests and Streaming Dense Couplings",
  "title_text": "GPU-Initiated Discrete Simulated Bifurcation: Low-Latency Requests and Streaming Dense Couplings",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:01Z",
  "url": "https://research.draconitic.com/paper/8w0vmwtjfesgf",
  "venue": "arXiv",
  "versions": []
}