Investigating Resilience of Loops in HPC Programs: A Semantic Approach with LLMs

Transient hardware faults, resulting from particle strikes, are significant concerns in HighPerformance Computing (HPC) systems. As these systems scale, the likelihood of soft errors rises. Traditional methods like Error-Correcting Codes (ECCs) and checkpointing address many of these errors, but some evade detection, leading to silent data corruptions (SDCs). This paper evaluates the resilience of HPC program loops, which are crucial for performance and error handling, by analyzing their computational patterns, known as the thirteen dwarfs of parallelism. We employ fault injection techniques to quantify SDC rates and utilize Large Language Models (LLMs) with prompt engineering to identify the loop semantics of the dwarfs in real source code. Our contributions include defining and summarizing loop patterns for each dwarf, quantifying their resilience, and leveraging LLMs for precise identification of these patterns. These insights enhance the understanding of loop resilience, aiding in the development of more resilient HPC applications.