检测并行软件中基于LLM调优指令重复的软错误

中文摘要 AI 辅助

我们提出了PaRID（并行指令重复），一个软件导向的软错误检测框架，仅需编译时开销即可用于多线程并行程序。PaRID解决了两个关键挑战：支持混合串行和并行区域的并行程序，以及在不依赖昂贵动态分析的情况下最小化性能开销。它结合了并行感知的代码转换与LLM调优的性能建模，并依据离线特征化研究得出的八个可泛化发现，实现了并行应用中快速的软错误检测。在NPB基准上的评估显示，PaRID将保护开销从平均162.79%降低至59.84%，实现了高达5倍的加速，同时保持了完整的错误检测有效性。

英文摘要

We propose PaRID (PaRallel Instruction Duplication), a software-directed soft error detection framework that requires only compile-time effort for multithreading parallel programs. PaRID addresses two key challenges: supporting parallel programs with mixed serial and parallel regions and minimizing performance overhead without relying on costly dynamic profiling. It combines parallel-aware code transformation with LLM-tuned performance modeling, guided by eight generalizable findings from an offline characterization study, to enable fast soft error detection in parallel applications. Evaluation on NPB benchmarks shows that PaRID reduces protection overhead from 162.79% to 59.84% on average and achieves up to 5x speedup while maintaining full error detection effectiveness.

发表机构

• University of Iowa（爱荷华大学）
• University of Florida（佛罗里达大学）

机构由 AI 辅助整理，请以论文原文为准。