GPU-Accelerated Simulated Oscillator Ising/Potts Machine Solving Combinatorial Optimization Problems

TL;DR

This paper introduces a GPU-based digital simulation framework for oscillator-based Ising and Potts machines, enabling high-speed, scalable solutions to complex combinatorial optimization problems with high accuracy.

Contribution

It presents the first GPU-accelerated simulation platform for large-scale oscillator-based Ising/Potts machines, improving speed, scalability, and precision over traditional CPU implementations.

Findings

Achieved up to 11295x speed-up over CPU implementations.

Demonstrated high accuracy up to 99% on benchmark problems.

Successfully solved large-scale max-cut and graph coloring 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.