GPU-Accelerated Simulated Oscillator Ising/Potts Machine Solving Combinatorial Optimization Problems
Abstract
Oscillator-based Ising machines (OIMs) and oscillator-based Potts machines (OPMs) have emerged as promising hardware accelerators for solving NP-hard combinatorial optimization problems by leveraging the phase dynamics of coupled oscillators. In this work, a GPU-accelerated simulated OIM/OPM digital computation framework capable of solving combinatorial optimization problems is presented. The proposed implementation harnesses the parallel processing capabilities of GPUs to simulate large-scale OIM/OPMs, leveraging the advantages of digital computing to offer high precision, programmability, and scalability. The performance of the proposed GPU framework is evaluated on the max-cut problems from the GSET benchmark dataset and graph coloring problems from the SATLIB benchmarks dataset, demonstrating competitive speed and accuracy in tackling large-scale problems. The results from simulations, reaching up to 11295x speed-up over CPUs with up to 99% accuracy, establish this framework as a scalable, massively parallelized, and high-fidelity digital realization of OIM/OPMs.
Cite
@article{arxiv.2505.22631,
title = {GPU-Accelerated Simulated Oscillator Ising/Potts Machine Solving Combinatorial Optimization Problems},
author = {Yilmaz Ege Gonul and Ceyhun Efe Kayan and Ilknur Mustafazade and Nagarajan Kandasamy and Baris Taskin},
journal= {arXiv preprint arXiv:2505.22631},
year = {2025}
}
Comments
6 pages, 3 figures, published in GLSVLSI 25