Preprint
GPU-Accelerated Simulated Oscillator Ising/Potts Machine Solving Combinatorial Optimization Problems
28 May 2025
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.
Metrics
4 Record Views
Details
- Title
- GPU-Accelerated Simulated Oscillator Ising/Potts Machine Solving Combinatorial Optimization Problems
- Creators
- Yilmaz Ege GonulCeyhun Efe KayanIlknur MustafazadeNagarajan KandasamyBaris Taskin
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Other Identifier
- 991022054404604721