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

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
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-08-15 至 2020-07-31

项目摘要

项目成果

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中文摘要
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英文摘要
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.
期刊论文(3)
专著(0)
科研奖励(0)
会议论文
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
影响因子: --
作者: [Duan, Shaohua, Parashar, Manish]
通讯作者: Parashar, Manish
DOI: 10.1109/ipdps.2018.00021
发表时间: 2018-05
期刊: 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
影响因子: --
作者: [Shaohua Duan;P. Subedi;K. Teranishi;Philip E. Davis;H. Kolla;Marc Gamell;M. Parashar]
通讯作者: Shaohua Duan;P. Subedi;K. Teranishi;Philip E. Davis;H. Kolla;Marc Gamell;M. Parashar
CIF21 DIBBs: EI: Virtual Data Collaboratory: A Regional Cyberinfrastructure for Collaborative Data Intensive Science
  • 批准号:
    2220826
  • 项目类别:
    Standard Grant
  • 资助金额:
    $400.0万
  • 财政年份:
    2021
  • 负责人:
    Ivan Rodero
  • 依托单位:
Collaborative Research: Framework: Data: NSCI: HDR: GeoSCIFramework: Scalable Real-Time Streaming Analytics and Machine Learning for Geoscience and Hazards Research
  • 批准号:
    2219975
  • 项目类别:
    Standard Grant
  • 资助金额:
    $89.91万
  • 财政年份:
    2021
  • 负责人:
    Ivan Rodero
  • 依托单位:
Collaborative Research: Framework: Data: NSCI: HDR: GeoSCIFramework: Scalable Real-Time Streaming Analytics and Machine Learning for Geoscience and Hazards Research
  • 批准号:
    1835692
  • 项目类别:
    Standard Grant
  • 资助金额:
    $89.91万
  • 财政年份:
    2019
  • 负责人:
    Ivan Rodero
  • 依托单位:
NSF Large Facilities Cyberinfrastructure Workshop
  • 批准号:
    1742969
  • 项目类别:
    Standard Grant
  • 资助金额:
    $6.51万
  • 财政年份:
    2017
  • 负责人:
    Ivan Rodero
  • 依托单位:
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