SPX: Collaborative Research: Cross-layer Application-Aware Resilience at Extreme Scale (CAARES)

SPX：协作研究：超大规模跨层应用程序感知弹性 (CAARES)

• 批准号: 1725649
• 负责人: Ivan Rodero
• 金额: $ 26.72万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1725649&HistoricalAwards=false
• 关键词: SPX; Collaborative; Research; Cross; layer

The increasing demands of science and engineering applications push the limits of current large-scale systems, and is expected to achieve exascale (10^18 FLOPS) performance early in the next decade. One of the lesser studied challenge at extreme scales is the reliability of the computing system itself, primarily due to the very large number of cores and components utilized and to the sharp decrease of the Mean Time Between Failures on such systems (in the order of tens of minutes). This project departs from the traditional single component fault management model, and explores how multiple software libraries (and application components) used in the context of a single parallel application can interact to provide the holistic fault management support necessary for parallel applications targeting capability computing. This exploration will not be limited to software developed using a single parallel programming paradigm, but will be extended to encompass the more challenging case where multiple programming paradigms can be combined to achieve a common goal, to simulate a set of large scale scientific applications in use today.  The goal of this project is to depart from the current siloed resilience mechanisms, and propose cross-layer composition solutions that can fundamentally address these resilience challenges at extreme scales. This exploration will not be limited to software developed using a single parallel programming paradigm, but will be extended to encompass the more challenging case where multiple programming paradigms can be combined to achieve a common goal, to simulate a set of large scale scientific applications in use today. More specifically, this proposal will address the following research challenges: (1) development of a theoretical foundation for a deeper understanding of the challenges and opportunities arising from combining different resilience models and methodologies; (2) design of a flexible programming abstraction to allow different resilience models and mechanisms to be combined to cooperate and address resilience in a more holistic manner; and (3) development of basic, programming paradigm independent, constructs necessary to implement cross-layer and domain-specific approaches to support resilience and to understand related performance / quality trade-offs. The proposed approach will be validated by exposing these generic abstractions in two different programming paradigms (MPI and OpenSHMEM), by creating and developing specialized concepts for each of these paradigms. This will enable the assessment of the validity of the concepts and the corresponding overheads imposed by the different software layers, using few software frameworks and applications.

科学和工程应用日益增长的需求推动了当前大规模系统的极限，预计在下一个十年的早期将实现exascale（10^18 FLOPS）性能。在极端规模下较少研究的挑战之一是计算系统本身的可靠性，主要是由于使用了非常大量的核心和组件，以及这些系统上的平均故障间隔时间急剧减少（大约几十分钟）。该项目从传统的单组件故障管理模型出发，并探讨如何在一个单一的并行应用程序的上下文中使用的多个软件库（和应用程序组件）可以交互，以提供必要的并行应用程序的能力计算的整体故障管理支持。这种探索将不仅限于使用单个并行编程范式开发的软件，而且将扩展到包括更具挑战性的情况，其中可以组合多个编程范式以实现共同目标，以模拟当今使用的一组大规模科学应用。该项目的目标是脱离当前孤立的弹性机制，并提出跨层组合解决方案，从根本上解决这些极端规模的弹性挑战。这种探索将不仅限于使用单一并行编程范式开发的软件，而是将被扩展以包括其中多个编程范例可以被组合以实现共同目标的更有挑战性的情况，来模拟当今使用的一系列大规模科学应用。更具体地说，这项建议将解决以下研究挑战：（1）为更深入地了解不同复原力模型和方法相结合所带来的挑战和机遇奠定理论基础;（2）设计灵活的方案编制抽象概念，以便将不同的复原力模型和机制相结合，以更全面的方式开展合作和解决复原力问题;以及（3）开发基本的、独立于编程范式的、实现跨层和特定领域方法所必需的结构，以支持弹性并理解相关的性能/质量权衡。所提出的方法将通过在两种不同的编程范式（MPI和OpenSHMEM）中暴露这些通用抽象来验证，通过为这些范式中的每一个创建和开发专门的概念。这将使评估的有效性的概念和相应的间接费用所施加的不同的软件层，使用几个软件框架和应用程序。

项目成果

Scalable Crash Consistency for Staging-based In-situ Scientific Workflows

基于分期的原位科学工作流程的可扩展崩溃一致性

• DOI: 10.1109/ipdpsw50202.2020.00068
• 发表时间: 2020
• 期刊: 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW
• 作者: Duan, Shaohua;Parashar, Manish
• 通讯作者: Parashar, Manish

Scalable Data Resilience for In-memory Data Staging

• DOI: 10.1109/ipdps.2018.00021
• 发表时间: 2018-05
• 期刊: 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: Shaohua Duan;P. Subedi;K. Teranishi;Philip E. Davis;H. Kolla;Marc Gamell;M. Parashar