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

2025-05-28

AI Summary

To address the challenge of efficiently solving NP-hard combinatorial optimization problems (e.g., Max-Cut, graph coloring) on GPUs, this work introduces the first large-scale, high-accuracy, and programmable GPU-accelerated digital simulation framework for oscillator-based Ising Machines (OIMs) and Potts Machines (OPMs), which exploit coupled-oscillator phase dynamics. Methodologically, the framework integrates CUDA-enabled parallel computing, fourth-order Runge–Kutta (RK4) numerical integration, graph-based optimization modeling, and problem-specific encoding techniques. Its key contribution is the first hardware-inspired yet digitally reliable simulation of OIMs/OPMs at the ten-thousand-node scale. Evaluated on GSET and SATLIB benchmarks, it achieves a 11,295× speedup over CPU-based solvers while maintaining 99% optimality rate—significantly advancing scalability and practical applicability for oscillator-based analog computing.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Hardware-aware ML

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Solving NP-hard combinatorial optimization problems efficiently
• Simulating large-scale OIM/OPMs using GPU acceleration
• Achieving high speed and accuracy in max-cut and graph coloring problems

Innovation

• GPU-accelerated simulated OIM/OPM framework
• Leverages GPU parallel processing for scalability
• High precision digital computing with programmability