Adaptive Bulk Search: Solving Quadratic Unconstrained Binary Optimization Problems on Multiple GPUs

Abstract

The quadratic unconstrained binary optimization (QUBO) is recently gathering attention in conjunction with quantum annealing (QA), since it is equivalent to finding the ground state of an Ising model. Due to the limitation of current QA systems, classical computers may outperform them. Researchers have thus been proposed to solve QUBO on FPGAs, GPUs, and special purpose processors. In this paper, we propose an adaptive bulk search (ABS), a framework for solving QUBO that can perform many searches in parallel on multiple GPUs. It supports fully-connected Ising models with up to 32k spins and 16-bit weights. In our ABS, a CPU host performs genetic algorithm (GA) while GPUs asynchronously perform local searches. A bottleneck for solving QUBO exists in the evaluation of the energy function, which requires computational cost for each solution. We show this can be reduced to in our ABS. The experimental results show that, with four NVIDIA GeForce RTX 2080 Ti GPUs, our framework can search up to 1.24 × 1012 solutions per second. We also show that our system quickly solves maximum cut and traveling salesman problems.