Performance optimization of hybrid GNN–LLM pipelines for code comparison

This study proposes a set of techniques for optimizing the performance of hybrid pipelines combining large language models (LLMs) and graph neural networks (GNN) for comparison of program code fragments. On the basis on previous work [1] on semantic normalization and graph embeddings, the main computational bottlenecks are identified and solutions are proposed including parallel processing, dynamic skipping of redundant operations, caching, and optimization of graph representation. The article c