Evaluation of portability and performance of an OpenMP5 offloaded Quantum-Inspired Evolutionary Optimization Across the GPU Ecosystem
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(Np Ng) for Np chromosomes and Ng 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×, 136×, and 155× over a single CPU core on the V100, A100, and MI300X, respectively, and 12×, 17×, and 16.6× over 72 host threads. Furthermore DetermineElite, the O(Np) selection of the generation-best chromosome, is found to be better suited to the host than to the device.
Related
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BibTeX
@misc{srikanth2026evaluation,
title = {{Evaluation of portability and performance of an OpenMP5 offloaded Quantum-Inspired Evolutionary Optimization Across the GPU Ecosystem}},
author = {Kasturi Venkata Srikanth and Ashish Singh and Ferdin Sagai Don Bosco and Aman Mittal and Abhishek Singh and Aditya Singh and Abhishek Chopra},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.30862},
eprint = {2609.30862},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30862},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Evaluation of portability and performance of an OpenMP5 offloaded Quantum-Inspired Evolutionary Optimization Across the GPU Ecosystem
AU - Kasturi Venkata Srikanth
AU - Ashish Singh
AU - Ferdin Sagai Don Bosco
AU - Aman Mittal
AU - Abhishek Singh
AU - Aditya Singh
AU - Abhishek Chopra
PY - 2026
DA - 2026/09/25
PB - arXiv
DO - 10.48550/arxiv.2609.30862
UR - https://arxiv.org/abs/2609.30862
AB - 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×, 136×, and 155× over a single CPU core on the V100, A100, and MI300X, respectively, and 12×, 17×, and 16.6× 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.
ER -