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.

在有增加系统故障的情况下，HPC的弹性是实现美国国家研究委员会进行Exascale科学的愿景的主要技术障碍。现有的技术主要基于检查点和重播，不再对具有更多的硬件和软件组件的新兴系统有效。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软件分析和综合技术来检测和缓解无声错误。开发的新方法可以将可调的弹性汇编为应用软件代码，并有可能改变HPC应用程序的未来后代的开发。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弹性问题提供更经济的解决方案。该项目将有助于实现美国NRC进行Exascale Science的愿景，这对于解决国家的紧急需求至关重要，例如新的能源，医疗保健和国家安全。该项目开发了自动化的程序分析技术，可从软件代码中识别不变性，并利用这些不景气来检测和减轻静音时间的静音时间。通过将应用程序软件代码视为白色框，它试图生成捕获预期程序行为的不变性。通过利用不变性作为正确性条件，它克服了检测沉默错误的主要障碍，即缺乏明显的症状。除了检测错误外，运行时监视器还使用不变性来智能扰动执行顺序或内存状态，以主动避免在运行时避免故障。当回滚恢复变得不可避免时，不间断的人被用作最大程度地减少重新执行开销的指导。拟议的方法和软件工具将在研究社区以及SCIDAC等来源的真实应用程序上评估。