SHF: Small: Program Analysis-based Makeover for HPC Application Resilience

SHF：小型：基于程序分析的 HPC 应用程序弹性改造

• 批准号: 1722710
• 负责人: Chao Wang
• 金额: $ 42万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1722710&HistoricalAwards=false
• 关键词: SHF; Small; Program; Analysis; based

HPC resilience in the presence of increased system failures is a major technical hurdle for realizing the vision of the US National Research Council for conducting exascale science. Existing techniques, based primarily on checkpoint and replay, are no longer effective for emerging systems with orders-of-magnitude more hardware and software components. This project aims to overcome the main limitation of existing techniques: the detection and mitigation of silent errors by developing and leveraging automated software analysis and synthesis techniques.The new methods under development can compile a tunable degree of resilience into the application software code, and have potential to transform the development of future generations of HPC applications. By treating the software code as white-boxes, as opposed to black-boxes, these new methods can provide significantly more economical solutions to the HPC resilience problem compared to existing techniques. The project will help realize the US NRC's vision of conducting exascale science, which is crucial for addressing the nation?s urgent needs in frontiers such as new energy, health care, and national security.This project develops automated program analysis techniques for identifying invariants from software code, and leveraging these invariants to detect and mitigate silent errors at run time. By treating the application software code as white-boxes, it seeks to generate invariants that capture the expected program behavior. By leveraging the invariants as correctness conditions, it overcomes the major hurdle in detecting silent errors, which is the lack of visible symptoms. In addition to detecting errors, the invariants are also used by runtime monitors to intelligently perturb the execution order or memory state to proactively avoid failures at run time. When the rollback recovery becomes inevitable, the invariants are used as guidance to minimize the re-execution overhead.The proposed methods and software tools are evaluated on real applications from the research community as well as sources such as SciDAC.

在系统故障增加的情况下，高性能计算的恢复能力是实现美国国家研究委员会开展百亿亿次科学的愿景的一个主要技术障碍。主要基于检查点和重放的现有技术对于具有更多数量级硬件和软件组件的新兴系统不再有效。该项目旨在克服现有技术的主要局限性：通过开发和利用自动化软件分析和综合技术来检测和减轻无声错误。正在开发的新方法可以将可调节程度的弹性编译到应用软件代码中，并具有改变未来几代 HPC 应用程序开发的潜力。通过将软件代码视为白盒而不是黑盒，与现有技术相比，这些新方法可以为 HPC 弹性问题提供更加经济的解决方案。该项目将有助于实现美国 NRC 开展百亿亿次科学的愿景，这对于解决国家在新能源、医疗保健和国家安全等前沿领域的迫切需求至关重要。该项目开发自动化程序分析技术，用于识别来自软件代码，并利用这些不变量来检测和减少运行时的静默错误。通过将应用程序软件代码视为白盒，它试图生成捕获预期程序行为的不变量。通过利用不变量作为正确性条件，它克服了检测无声错误的主要障碍，即缺乏可见的症状。除了检测错误之外，运行时监视器还使用不变量来智能地扰乱执行顺序或内存状态，以主动避免运行时的故障。当回滚恢复不可避免时，不变量被用作指导，以最小化重新执行开销。所提出的方法和软件工具在来自研究社区以及 SciDAC 等来源的实际应用中进行了评估。