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

合作研究：SHF：小型：大规模学习容错

• 批准号: 2135309
• 负责人: Padma Raghavan
• 金额: $ 30万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2135309&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Small; Learning; Collaborative; Research; SHF; Small; Learning

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.

在计算机辅助设计和对工程系统（例如汽车或半导体芯片）的分析中，在高性能计算机上模拟了计算模型，以表征和评估关键属性。这种高性能计算系统的巨大规模，例如，超过200亿晶体管（世界上最快的超级计算机之一），增加了来自宇宙辐射或诸如宇宙辐射或芯片芯片电压波动等事件的瞬时硬件故障的可能性。此类错误的可能性及其负面影响会进一步增加，因为这种模拟通常很长，并且单个数据字段或变量的损坏可能需要数周到几个月的重新计算，然后才能做出重大决策。该项目将开发自动化的方法，这些方法为这些应用程序带来了可容纳硬件故障的容错，这些应用不仅在多个工业领域都广泛使用，还可以提高气候或天气模型的预测能力以帮助关键决策。传统的耐故障方案可以是特定于应用程序的，需要重大的程序员精力重新设计或自定义大型软件，或者需要定期存储所有或大多数数据以限制恢复性，从而限制其可扩展性，从而限制其可扩展性，从而限制其可扩展性。该项目旨在通过为新的耐故障方案提供理论基础来解决这些局限性，该方案适用于基于迭代数值模拟的广泛应用程序，这些模拟会随着离散的空间域而随着时间的流逝而演变。该项目基于这样的前提：在此类基于物理的应用程序中，跨时间步骤（迭代）和空间域的解决方案向量组件的变化速率是自动确定应使用的关键计算变量并动态选择应应用的保障技术类型的关键指标。调查人员将追求三个关键方向：（i）通过开发弹性梯度指标来表征应用程序的固有弹性，（ii）制定和测试，以使弹性梯度的水平和类型适应弹性梯度的水平和类型，以降低计算范围的稳定性和（iii II）的构建框架，并降低计算范围，并以（iii II）进行构建框架，并系统的能力使用近似计算和共同安排技术。调查人员还将与应用和运行时系统开发人员紧密合作，以寻求更广泛的容忍度框架，开发专业的本科生和研究生课程进行学生培训，并为高中生提供研究经验。该奖项反映了NSF的法定任务，并通过该基金会的知识绩效和广泛的影响来通过评估来进行评估，并通过评估值得进行评估。