{
  "abstract": "Quantum-inspired evolutionary optimization (QIEO) is a new class of population-based metaheuristic optimization algorithms which represents design variables as a set of qubits and searches a continuous, multi-dimensional landscape through rotation of the qubit's amplitude pair. Every generation rotates those amplitudes toward a single elite, which corresponds to that generation's best. The per-generation cost scales as $O(N_p N_g)$ for $N_p$ chromosomes and $N_g$ genes (decision variables). Production use of such solvers is rarely confined to a single machine class. Prototypes are run on laboratory servers- before moving to rented cloud workstations for more involved campaigns. The largest problems are reserved for leadership-class accelerators. This paper asks whether a \\emph{single} OpenMP~5 source of QIEO, offloaded with \\texttt{\\#pragma omp target}, is a viable production path in each of those settings. We report three independent, campaigns of the 0/1 knapsack problem against a same-source multi-core Intel CPU baseline. The study comprises approximately 3,000 runs spanning varying chromosome and gene counts, evaluated using both chromosome-level and gene-level offload strategies on the NVIDIA Tesla V100 SXM2, NVIDIA A100 80GB, and AMD Instinct MI300X GPUs. Deployment-specific nuances such as Volta's constant-memory cliffs, Ampere's L2 persistence and \\texttt{cp.async}, CDNA~3's Infinity Cache and XCD occupancy are addressed to ensure high performance of these platforms. Results reveal gene-parallel offload achieved geometric-mean speedups of 90$\\times$, 136$\\times$, and 155$\\times$ over a single CPU core on the V100, A100, and MI300X, respectively, and 12$\\times$, 17$\\times$, and 16.6$\\times$ over 72 host threads. Furthermore DetermineElite, the $O(N_p)$ selection of the generation-best chromosome, is found to be better suited to the host than to the device.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.30862",
  "author_total": 7,
  "authors": [
    {
      "name": "Kasturi Venkata Srikanth"
    },
    {
      "name": "Ashish Singh"
    },
    {
      "name": "Ferdin Sagai Don Bosco"
    },
    {
      "name": "Aman Mittal"
    },
    {
      "name": "Abhishek Singh"
    },
    {
      "name": "Aditya Singh"
    },
    {
      "name": "Abhishek Chopra"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0030rp470q467.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.30862",
  "fulltext_url": null,
  "id": "0030rp470q467",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.30862",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.PF"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "1708|Parallel Computing and Optimization Techniques"
    ]
  ],
  "openalex": "W7214629763",
  "pdf_url": "https://arxiv.org/pdf/2609.30862",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0030rp470q467.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:57Z",
      "native_id": "2609.30862",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.30862"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Evaluation of portability and performance of an OpenMP5 offloaded Quantum-Inspired Evolutionary Optimization Across the GPU Ecosystem",
  "title_text": "Evaluation of portability and performance of an OpenMP5 offloaded Quantum-Inspired Evolutionary Optimization Across the GPU Ecosystem",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:57Z",
  "url": "https://research.draconitic.com/paper/0030rp470q467",
  "venue": "arXiv",
  "versions": []
}