Investigation of Performance and Scalability of a Quantum-Inspired Evolutionary Optimizer (QIEO) on NVIDIA GPU
Aman Mittal, Kasturi Venkata Sai Srikanth, Ferdin Sagai Don Bosco, Abhishek Singh, Rut Lineswala, Abhishek Chopra

TL;DR
This paper evaluates the performance and scalability of a GPU-accelerated Quantum Inspired Evolutionary Optimizer (QIEO) on large-scale 01 Knapsack problems, demonstrating significant speedups through optimized memory and kernel configurations.
Contribution
It systematically analyzes GPU-based QIEO performance, providing insights into memory strategies and kernel tuning for large-scale combinatorial optimization.
Findings
Constant memory yields best performance up to hardware limits.
Global memory and tiling are necessary beyond certain problem sizes.
Careful tuning of memory and kernel configurations is crucial for efficiency.
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…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Quantum Computing Algorithms and Architecture · Evolutionary Algorithms and Applications
