Problem
Research questions and friction points this paper is trying to address.
Soft Errors
Parallel Software
Performance Overhead
Detecting Soft Errors in Parallel Software with LLM-tuned Instruction Duplication
🤖 AI Summary
本文提出PaRID框架，通过编译时努力和LLM调整性能建模，解决并行软件中软错误检测问题，减少保护开销并提高检测速度。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Instruction Duplication
Parallel Software
Soft Error Detection
LLM-tuned Performance Modeling
Compile-time Effort
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