Investigation of Performance and Scalability of a Quantum-Inspired Evolutionary Optimizer (QIEO) on NVIDIA GPU

Abstract

Quantum inspired evolutionary optimization leverages quantum computing principles like superposition, interference, and probabilistic representation to enhance classical evolutionary algorithms with improved exploration and exploitation capabilities. Implemented on NVIDIA Tesla V100 SXM2 GPUs, this study systematically investigates the performance and scalability of a GPU-accelerated Quantum Inspired Evolutionary Optimizer applied to large scale 01 Knapsack problems. By exploiting CUDA`s parallel processing capabilities, particularly through optimized memory management and thread configuration, significant speedups and efficient utilization of GPU resources is demonstrated. The analysis covers various problem sizes, kernel launch configurations, and memory models including constant, shared, global, and pinned memory, alongside extensive scaling studies. The results reveal that careful tuning of memory strategies and kernel configurations is essential for maximizing throughput and efficiency, with constant memory providing superior performance up to hardware limits. Beyond these limits, global memory and strategic tiling become necessary, albeit with some performance trade offs. The findings highlight both the promise and the practical constraints of applying QIEO on GPUs for complex combinatorial optimization, offering actionable insights for future large scale metaheuristic implementations.