通过谱延迟校正中的自适应性实现对软故障的弹性
Resilience Against Soft Faults through Adaptivity in Spectral Deferred Correction
AI总结：
针对超级计算机硬件复杂度提升带来的瞬态故障问题，利用谱延迟校正（SDC）的自适应步长选择进展，证明自适应SDC可检测校正瞬态故障，性能媲美专用弹性策略Hot Rod。
摘要：
随着超级计算机硬件复杂度不断提升，其对故障的敏感性也随之增加，需采取措施保障结果的正确性。部分数值算法具备某些特性，可从部分类型的故障中恢复。已有研究证明，自适应Runge-Kutta方法无需额外计算成本即可提供对瞬态故障的弹性。借助可生成任意阶方法的迭代数值时间步进方案——谱延迟校正（SDC）的自适应步长选择最新进展，我们证明自适应SDC同样能够检测并校正瞬态故障，其性能与专用弹性策略Hot Rod相当。
英文摘要：
As supercomputers grow in hardware complexity, their susceptibility to faults increases and measures need to be taken to ensure the correctness of results. Some numerical algorithms have certain characteristics that allow them to recover from some types of faults. It has been demonstrated that adaptive Runge-Kutta methods provide resilience against transient faults without adding computational cost. Using recent advances in adaptive step size selection for spectral deferred correction (SDC), an iterative numerical time stepping scheme that can produce methods of arbitrary order, we show that adaptive SDC can also detect and correct transient faults. Its performance is found to be comparable to that of the dedicated resilience strategy Hot Rod.
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。
