Collaborative Research: PPoSS: Planning: Performance Scalability, Trust, and Reproducibility: A Community Roadmap to Robust Science in High-throughput Applications

协作研究:PPoSS:规划:性能可扩展性、信任和可重复性:高通量应用中稳健科学的社区路线图

基本信息

  • 批准号:
    2028923
  • 负责人:
  • 金额:
    $ 9万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-10-01 至 2022-09-30
  • 项目状态:
    已结题

项目摘要

This project is focused on a critical issue in computational science. As scientists in all fields increasingly rely on high-throughput applications (which combine multiple components into increasingly complex multi-modal workflows on heterogeneous systems), the increasing complexities of those applications hinder the scientists’ ability to generate robust results. The project recruits a cross-disciplinary community working together to define, design, implement, and use a set of solutions for robust science. In so doing, the community defines a roadmap that enables high-throughput applications to withstand and overcome adverse conditions such as heterogeneous, unreliable architectures at all scales including extreme scale, rigorous testing under uncertainties, unexplainable algorithms (e.g., in machine learning), and black-box methods. The project’s novelties are its comprehensive, cross-disciplinary study of high-throughput applications for robust scientific discovery from hardware and systems all the way to policies and practices.Through three virtual mini-workshops called virtual world cafes, this project engages a community of scientists at campuses (through the Computing Alliance of Hispanic-Serving Institutions [CAHSI], the Coalition for Academic Scientific Computing [CASC], and the Southern California Earthquake Center [SCEC]), at national laboratories, and in industry. The scientists participate in defining scalability, trust, and reproductivity in an initial set of high-throughput applications; identifying a set of experimental practices that support the in-concert successful progress of these applications’ workflows; advancing towards a vision of general hardware and software solutions for robust science by evaluating the generality and transferability of experimental practices and by identifying any missing parts; and defining a research agenda for the next-generation workflows.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.
该项目的重点是计算科学中的关键问题。随着所有领域的科学家越来越依赖于高通量应用(将多个组件结合到异质系统上日益复杂的多模式工作流程中),这些应用的复杂性的增加阻碍了科学家产生强大结果的能力。该项目招募了一个跨学科的社区,共同努力定义,设计,实施和使用一套用于强大科学的解决方案。这样一来,社区定义了一个路线图,该路线图可以实现高通量应用,以承受和克服不利条件,例如在所有尺度上的异质,不可靠的体系结构,包括极端尺度,不确定性下的严格测试,无法确定的算法(例如,机器学习中)以及黑包装方法。 The project’s novelties are its comprehensive, cross-disciplinary study of high-throughput applications for robust scientific discovery from hardware and systems all the way to policies and practices.Through three virtual mini-workshops called virtual world cafes, this project engages a community of scientists at campuses (through the Computing Alliance of Hispanic-Serving Institutions [CAHSI], the Coalition for Academic Sc​​ientific Computing [CASC], and国家实验室和工业的南加州地震中心[SCEC])。科学家参与在初始高通量应用程序集中定义可扩展性,信任和生殖力;确定一组实验实践,以支持这些应用程序工作流程的CONCERT成功进展;通过评估实验实践的一般性和可传递性并确定任何缺失的零件,可以朝着对鲁棒科学的一般硬件和软件解决方案的愿景前进;并定义了下一代工作流程的研究议程。该奖项反映了NSF的法定任务,并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估,被认为是珍贵的支持。

项目成果

期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Research-Based Course Module to Study Non-determinism in High Performance Applications
用于研究高性能应用中的非确定性的研究型课程模块
A Roadmap to Robust Science for High-throughput Applications: The Scientists’ Perspective
高通量应用的稳健科学路线图:科学家的观点
  • DOI:
    10.1109/escience51609.2021.00044
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Taufer, M.;Deelman, E.;da Silva, R. Ferreira;Estrada, T.;Hall, M.
  • 通讯作者:
    Hall, M.
A Roadmap to Robust Science for High-throughput Applications: The Developers’ Perspective
高通量应用的稳健科学路线图:开发人员的视角
  • DOI:
    10.1109/cluster48925.2021.00068
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Taufer, M.;Deelman, E.;Silva, R. Ferreira;Estrada, T.;Hall, M.;Livny, M.
  • 通讯作者:
    Livny, M.
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Michela Taufer其他文献

Enhancing Scientific Research with FAIR Digital Objects in the National Science Data Fabric
利用国家科学数据结构中的 FAIR 数字对象加强科学研究
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Michela Taufer;Heberth Martinez;Jakob Luettgau;Lauren Whitnah;G. Scorzelli;P. Newell;Aashish Panta;P. Bremer;Douglas Fils;Christine R. Kirkpatrick;V. Pascucci;Kathryn Mohror;J. Shalf
  • 通讯作者:
    J. Shalf
Integrating FAIR Digital Objects (FDOs) into the National Science Data Fabric (NSDF) to Revolutionize Dataflows for Scientific Discovery
将 FAIR 数字对象 (FDO) 集成到国家科学数据结构 (NSDF) 中,彻底改变科学发现的数据流
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Michela Taufer;Heberth Martinez;Jakob Luettgau;Lauren Whitnah;†. GiorgioScorzelli;†. PaniaNewel;Aashish Panta;Timo Bremer;§. DougFils;¶. ChristineR.Kirkpatrick;Nina McCurdy;V. Pascucci;U. Knoxville;†. U.Utah;R. LLNL ‡;Research Center
  • 通讯作者:
    Research Center

Michela Taufer的其他文献

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{{ truncateString('Michela Taufer', 18)}}的其他基金

EAGER: A Comprehensive Approach for Generating, Sharing, Searching, and Using High-Resolution Terrain Parameters
EAGER:生成、共享、搜索和使用高分辨率地形参数的综合方法
  • 批准号:
    2334945
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF: Small: Model-driven Design and Optimization of Dataflows for Scientific Applications
协作研究:SHF:小型:科学应用数据流的模型驱动设计和优化
  • 批准号:
    2331152
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
SHF: Small: Methods, Workflows, and Data Commons for Reducing Training Costs in Neural Architecture Search on High-Performance Computing Platforms
SHF:小型:降低高性能计算平台上神经架构搜索训练成本的方法、工作流程和数据共享
  • 批准号:
    2223704
  • 财政年份:
    2022
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
Collaborative Research: Elements: SENSORY: Software Ecosystem for kNowledge diScOveRY - a data-driven framework for soil moisture applications
协作研究:要素:SENSORY:知识发现的软件生态系统 - 土壤湿度应用的数据驱动框架
  • 批准号:
    2103845
  • 财政年份:
    2021
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
Collaborative Research: EAGER: Advancing Reproducibility in Multi-Messenger Astrophysics
合作研究:EAGER:提高多信使天体物理学的可重复性
  • 批准号:
    2041977
  • 财政年份:
    2020
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
SHF: Medium: Collaborative Research: ANACIN-X: Analysis and modeling of Nondeterminism and Associated Costs in eXtreme scale applications
SHF:中:协作研究:ANACIN-X:极端规模应用中的非确定性和相关成本的分析和建模
  • 批准号:
    1900888
  • 财政年份:
    2019
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant
Collaborative: EAGER: Exploring and Advancing the State of the Art in Robust Science in Gravitational Wave Physics
合作:EAGER:探索和推进引力波物理学稳健科学的最新技术
  • 批准号:
    1841399
  • 财政年份:
    2018
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
Collaborative: EAGER: Exploring and Advancing the State of the Art in Robust Science in Gravitational Wave Physics
合作:EAGER:探索和推进引力波物理学稳健科学的最新技术
  • 批准号:
    1823372
  • 财政年份:
    2018
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
SHF:Medium:Collaborative Research:A comprehensive methodology to pursue reproducible accuracy in ensemble scientific simulations on multi- and many-core platforms
SHF:中:协作研究:在多核和众核平台上追求集合科学模拟的可重复精度的综合方法
  • 批准号:
    1841552
  • 财政年份:
    2018
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
BIGDATA: IA: Collaborative Research: In Situ Data Analytics for Next Generation Molecular Dynamics Workflows
BIGDATA:IA:协作研究:下一代分子动力学工作流程的原位数据分析
  • 批准号:
    1841758
  • 财政年份:
    2018
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant

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相似海外基金

Collaborative Research: PPoSS: Large: A Full-stack Approach to Declarative Analytics at Scale
协作研究:PPoSS:大型:大规模声明性分析的全栈方法
  • 批准号:
    2316161
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant
Collaborative Research: PPoSS: LARGE: Research into the Use and iNtegration of Data Movement Accelerators (RUN-DMX)
协作研究:PPoSS:大型:数据移动加速器 (RUN-DMX) 的使用和集成研究
  • 批准号:
    2316176
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant
Collaborative Research: PPoSS: Large: A Full-stack Approach to Declarative Analytics at Scale
协作研究:PPoSS:大型:大规模声明性分析的全栈方法
  • 批准号:
    2316158
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant
Collaborative Research: PPoSS: LARGE: Cross-layer Coordination and Optimization for Scalable and Sparse Tensor Networks (CROSS)
合作研究:PPoSS:LARGE:可扩展和稀疏张量网络的跨层协调和优化(CROSS)
  • 批准号:
    2316201
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
Collaborative Research: PPoSS: LARGE: Cross-layer Coordination and Optimization for Scalable and Sparse Tensor Networks (CROSS)
合作研究:PPoSS:LARGE:可扩展和稀疏张量网络的跨层协调和优化(CROSS)
  • 批准号:
    2316203
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant
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