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PESA: error sensitivity analysis tool for floating-point computational programs

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Abstract

Floating-point programs are currently extensively applied across various domains of scientific computing. Within such computational programs, there is a propensity to utilize high-precision floating-point variables to ensure the accuracy of the results. However, this practice concurrently leads to a decline in program performance. To address this issue, researchers have aimed to reduce the precision requirements of variables in programs without significant impact on results. Current mixed-precision tools utilize differential derivation to compute variable sensitivity, but this method is cumbersome and can cause memory crashes in intensive computational programs. This paper presents program error sensitivity analysis (PESA), an error sensitivity analysis tool based on variance decomposition, optimized for floating-point programs using LLVM. PESA uses the Sobol method to perform error sensitivity analysis and provides the user with the magnitude of error sensitivity for each variable in the program. Experimental results show that PESA produces error analysis results consistent with other tools and outperforms other sensitivity analysis tools overall by testing six benchmark files and small applications.

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  1. Information Engineering University, Zhengzhou, 450001, Henan, China

    Mengqi Cui, Jinchen Xu, Yuchang Zhou, Hongru Yang, Liguang Ji & Bei Zhou

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  1. Mengqi Cui
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  2. Jinchen Xu
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  3. Yuchang Zhou
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  4. Hongru Yang
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  5. Liguang Ji
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  6. Bei Zhou
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Correspondence to Bei Zhou.

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Cui, M., Xu, J., Zhou, Y. et al. PESA: error sensitivity analysis tool for floating-point computational programs. J Supercomput 81, 477 (2025). https://doi.org/10.1007/s11227-025-06962-z

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