OAC Core: Interpretable Resilience Analysis Platform for Scientific Workflow Applications

多年来，科学家们一直在不断改进被忽视的弹性模拟的性能。这种方法是由于缺乏对弹性分析中的“因果关系”的理解所驱动的。虽然目前的弹性分析工具仍然缺乏透明度和可解释性，但至关重要的是，要促进弹性分析的重要性，并教育科学家了解其重要性。这个项目的新奇之处在于重新定义了弹性分析的可解释性和可解释性。这种方法与现有的努力有很大的不同。它可以解释或识别这些预测背后的逻辑，并区分基于不同理论的现有工具的功能和用法。该项目的影响包括设计一个新的弹性评估系统，使用可视化和DevOps来实现透明的弹性分析、漏洞定位和弹性持续集成的自动化。该项目与美国国家科学基金会和美国能源部赞助的超级计算中心合作，采用了已经证明成功的系统。研究生和本科生，特别是来自代表性不足群体的学生，将接受多学科的培训，这将使他们能够在日益跨学科的计算机/科学研究领域取得成功。该项目建立在现有知识的基础上，创造了一种新的有洞察力的方法，使科学应用的弹性特性能够在下一代高性能计算系统中不可避免地存在激增的软错误的情况下进行评估。该项目将进一步明确、洞察和理解系统在运行高性能计算科学工作负载时的行为，这些工作负载由并行模拟组成，用于数据生成、大数据分析和机器学习，以提取科学研究中的数据见解。该项目提出1.)设计和实现一个错误传播分析平台，该平台可以创建代码关键路径和关键部分的可解释可视化；2)分析，允许领域科学家在模拟代码上比较和对比不同的弹性模型；3)一个持续的弹性评估（弹性CI），它可以集成到一个标准的持续集成中，以实现过程的自动化；据此，已提交版本之间的弹性属性将作为标准报告交付给开发人员，并支持超大规模科学应用程序的DevOps；4)量子化学工作流将作为驱动应用参与评估。项目的结果，如教程、收集的数据和可视化软件系统，可以鼓励应用程序开发人员结合经济有效的容错策略。此外，调查人员将把研究成果纳入新的课程和教程，为劳动力培训。该项目将参与并推进与该行业的合作伙伴关系，以实现商业化。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

For years, scientists have continued to improve the performance of the simulations where resilience was neglected. This approach was driven by a lack of understanding of the "cause and effect" in resilience analysis. While the current resilience analysis tool continues to lack transparency and interpretability, it is critical that the importance of resilience analysis is promoted and that scientists are educated on its criticality. This project's novelties are redefining the resilience analysis in terms of interpretability and explainability. The approach is significantly different from existing endeavors. It can explain or identify the logic behind these predictions and differentiate the functions and usages of the existing tools built on different theories. The project's impacts include designing a new resilience assessment system using visualization and DevOps to enable transparent resilience analysis, vulnerability positioning, and automation of resilience continuous integration. The project work with NSF and DoE-sponsored supercomputing centers to adopt the system with proven success. Graduate and undergraduate students, especially from underrepresented groups, will be trained in multiple disciplines that will enable them to have successful careers in computing/scientific research areas that are becoming increasingly interdisciplinary.This project builds upon existing knowledge to create a new insightful approach that enables the resilience property of scientific applications to be assessed under the inevitable existence of surging soft errors in next-generation high-performance computing systems. This project will bring further clarity, insight, and understanding into how systems behave while running high-performance computing scientific workloads composed of parallel simulations for data generation, big data analytics, and machine learning to extract data insights in scientific research. The project proposes 1.) the design and implementation of an error propagation analysis platform, which creates interpretable visualization of the critical paths and critical sections of the codes; 2.) analytics to allow domain scientists to compare and contrast the different resilience models on the simulation codes; 3.) a continuous resilience assessment (Resilience CI) that can be integrated into a standard continuous integration to automate the procedure; whereby the resilience property between committed versions will be delivered to developers as a standard report and to support the DevOps of exa-scale scientific applications; and 4.) quantum chemistry workflow will participate in the evaluation as the driver applications. The project's outcomes, such as tutorials, collected data, and the visualization software system, can encourage the application developers to incorporate cost-effective fault tolerance strategies. In addition, the investigators will incorporate research outcomes in new courses and tutorials for the workforce training. The project will engage and advance the partnership with the industry for commercialization.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.