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arXiv AI By Yafan Huang, Guanpeng Li

Detecting Soft Errors in Parallel Software with LLM-tuned Instruction Duplication

Read the original on arXiv AI →

The paper introduces PaRID, a software-directed soft error detection framework that uses parallel instruction duplication and LLM-tuned performance modeling. PaRID is designed to work at compile time for multithreaded programs, handling mixed serial and parallel regions while keeping performance overhead low. Experiments on NPB benchmarks show that PaRID cuts protection overhead from 162.79% to 59.84% on average and can achieve up to a 5× speedup while still detecting all errors.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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