Collaborative Research: SHF: Small: Learning Fault Tolerance at Scale

在汽车或半导体芯片等工程系统的计算机辅助设计和分析中，计算模型是在高性能计算机上模拟的，以表征和评估关键属性。这种高性能计算系统的庞大规模，例如Summit(世界上速度最快的超级计算机之一)中超过200亿个晶体管，增加了因宇宙辐射或处理器-芯片电压波动等事件而发生瞬时硬件故障的可能性。这种错误及其负面影响的可能性进一步增加，因为这种模拟通常是长期运行的，单个数据字段或变量的损坏可能需要数周至数月的重新计算，才能做出关键决策。该项目将开发自动化方法，为这类应用程序的硬件故障带来容错能力，这些应用程序不仅在多个工业部门广泛使用，而且还可以增加气候或天气模型的预测能力，以帮助关键决策。传统的容错方案可以是特定于应用程序的，需要程序员花费大量精力来重新设计或定制大型软件，也可以是与应用程序无关的，其中所有或大多数数据定期冗余存储以允许恢复，因此由于其巨大的内存和处理开销而限制了其可伸缩性。该项目试图通过为一类新的容错方案提供理论基础来解决这些限制，这些容错方案适用于基于离散空间域上随时间演变的迭代数值模拟的广泛应用。该项目的前提是，在这种基于物理的应用中，解向量分量在时间步长(迭代)和空间域中的变化率是自动识别关键计算变量、监控其演变和动态选择应应用的保护技术类型的关键度量。调查人员将寻求三个主要方向：(I)通过制定弹性梯度指标来表征应用程序的内在弹性，(Ii)开发和测试容错方案，使保护的级别和类型适应弹性梯度，以减少计算开销和增加可扩展性，以及(Iii)构建一个基于自动决策的学习框架，用于根据系统使用近似计算和协同调度技术的能力自适应地选择容错方法。调查人员还将与应用程序和运行时系统开发人员密切合作，寻求此容错框架的更广泛使用，为学生培训开发专门的本科和研究生课程，并为高中生提供研究经验。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In computer-aided design and analysis of engineered systems such as automobiles or semiconductor chips, computational models are simulated on high-performance computers to characterize and evaluate key attributes. The sheer scale of such high-performance computing systems, e.g., over 20 billion transistors in Summit (one of the world's fastest supercomputers), increases the likelihood of transient hardware faults from events such as cosmic radiation or processor-chip voltage fluctuations. The likelihood of such errors and their negative impacts are further increased as such simulations are typically long running, and the corruption of a single data field or variable may require weeks to months of re-computations before critical decisions can be made. This project will develop automated approaches that bring fault tolerance to hardware faults for such applications which are widely used not only across multiple industrial sectors but to also increase the predictive power of climate or weather models to aid critical decision making. Traditional fault-tolerant schemes can be either application-specific, requiring significant programmer effort to redesign or customize large-scale software, or application-agnostic where all or most data are redundantly stored periodically to allow for recovery, thus limiting their scalability due to their significant memory and processing overheads. This project seeks to address these limitations by providing a theoretical foundation for a new class of fault-tolerant schemes that are suitable for the broad array of applications based on iterative numerical simulations that evolve over time on discretized spatial domains. This project is based on the premise that in such physics-based applications, the rate of change of the solution vector components across time steps (iterations) and spatial domains is a key metric to automatically identifying the critical computational variables, monitoring their evolution, and dynamically selecting the type of safeguarding techniques that should be applied. The investigators will pursue three key directions: (i) characterizing the intrinsic resiliency of the application by developing resiliency gradient metrics, (ii) developing and testing fault-tolerance schemes that adapt the level and type of protection to the resiliency gradient with the goal of reducing computational overheads and increasing scalability, and (iii) constructing an automatic online decision-based learning framework for adaptively selecting fault-tolerance methods in relation to the system's ability to use approximate computing and co-scheduling techniques. The investigators will also work closely with application and runtime system developers to seek broader use of this fault tolerance framework, develop specialized undergraduate and graduate curriculum for student training, and offer research experiences to high school students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.