On the Potential of Large Language Models in Parallel Systems: A Systematic Analysis

Parallel systems adaptively optimize complex physi-cal systems that involve both engineering and social complexities based on optimal strategies learned from large amounts of artificial data. Despite their great success in many applications, existing parallel systems still face challenges such as insufficient data for artificial systems, inefficient implementation of computational experiments, and unexplainable interaction during parallel execution. Recent advances in large language models (LLMs) have endowed them with diverse emergent abilities, such as synthesizing virtual data, assisting humans with complex tasks, designing and executing plans, and engaging in natural language conversations. These capabilities provide parallel systems with new opportunities for performance improvement. This paper systematically reviews the state-of-the-art research on LLMs and their applications in parallel system-related domains, aiming to provide valuable insights for researchers in related fields.