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arXiv 1312.2674math.NAcs.MScs.NA
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数值时间推进方案中的静默错误检测

Silent error detection in numerical time-stepping schemes

Stanford University(斯坦福大学);HP Labs(惠普实验室)

Austin R. Benson, Sven Schmit, Robert Schreiber

AI总结:

本文提出在应用层面检测静默错误的新方法,通过频繁比较计算值与廉价检查计算结果的差异,利用数值分析为常微分方程和偏微分方程提供检查计算方法,通过热方程和纳维-斯托克斯方程验证了该方法的有效性。

摘要:

由于硬件或低级软件问题导致的错误,如果被检测到,可以通过各种方案修复,例如从检查点重新计算。静默错误是逃过了低级错误检测的应用状态错误。在极端规模下,机器每秒可以执行天文数字的操作,静默错误威胁到计算结果的有效性。我们提出了一种新的检测静默错误的范式。我们的核心思想是频繁比较计算值与由廉价检查计算提供的值,并基于两个输出序列的差异构建错误检测器。数值分析为我们提供了用于求解初值问题的可用检查计算,这可能是计算科学中最常见的问题。在这里,我们提供、优化并测试了基于Runge-Kutta和线性多步方法的ODEs方法,以及基于隐式和显式有限差分方案的PDEs方法。我们以热方程和纳维-斯托克斯方程为例。在人为注入错误的测试中,这种方法几乎可以检测到所有有意义的错误,而不会显著减慢速度。

英文摘要:

Errors due to hardware or low level software problems, if detected, can be fixed by various schemes, such as recomputation from a checkpoint. Silent errors are errors in application state that have escaped low-level error detection. At extreme scale, where machines can perform astronomically many operations per second, silent errors threaten the validity of computed results. We propose a new paradigm for detecting silent errors at the application level. Our central idea is to frequently compare computed values to those provided by a cheap checking computation, and to build error detectors based on the difference between the two output sequences. Numerical analysis provides us with usable checking computations for the solution of initial-value problems in ODEs and PDEs, arguably the most common problems in computational science. Here, we provide, optimize, and test methods based on Runge-Kutta and linear multistep methods for ODEs, and on implicit and explicit finite difference schemes for PDEs. We take the heat equation and Navier-Stokes equations as examples. In tests with artificially injected errors, this approach effectively detects almost all meaningful errors, without significant slowdown.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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