Virtual Data Set Services Enabling New Science at NSF Facilities

虚拟数据集服务在 NSF 设施中实现新科学

基本信息

  • 批准号:
    1841531
  • 负责人:
  • 金额:
    $ 150万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-10-01 至 2021-09-30
  • 项目状态:
    已结题

项目摘要

Scientific facilities supported by the National Science Foundation such as the Daniel K. Inouye Solar Telescope (DKIST), National Center for Atmospheric Research (NCAR), and National Ecological Observatory Network (NEON) collect enormous quantities of valuable data about the world in which we live. These data can be used for scientific and societal benefit: to make breakthrough discoveries about sun's behavior and magnetic field, and our changing environment; to improve the speed and accuracy of forecasting of severe storms and destructive wildfires; to predict disruptions to electrical systems from solar flares; and many other purposes. Before such vital scientific data can be used effectively, they must be delivered rapidly, efficiently, and reliably to the people who need them. Researchers need interactive community access to hard-to-obtain data located in large data archives. Because the NCAR, DKIST, and NEON data archives cannot feasibly provide the computing resources needed for all analyses, end-user scientists need to be able to define, navigate, download, and analyze data subsets. Current web-based tools are not up to these tasks. The Virtual Data Set Services Enabling New Science at NSF Facilities project will tackle this challenge by developing new methods for organizing, packaging, and rapidly transporting data. A key innovation will be the development of methods for defining, sharing, and manipulating "virtual data sets," data collections extracted "on the fly" from the vast holdings of scientific facilities for a specific purpose. A researcher may define a virtual data set much as a shopper assembles products in an online "shopping cart." Once defined, a virtual data set can then be transferred to a remote computer for analysis, shared with colleagues, or extended for future projects. We will develop new services to (a) enable definition of, navigation over, and selective access to virtual data sets from petascale data archives of the scientific facilities, and (b) ensure reliable, automated, efficient, and secure replication and access of entire data sets or data subsets between a petascale data archive and other locations, to include both end user computers and remote mirrors intended to accelerate data access by community members. These new services will be constructed on top of the Globus platform, already heavily used within NCAR's Research Data Archive (RDA) and many other research data centers. The ultimate aim is to integrate the new services into operational systems in collaboration with DKIST, NEON, and NCAR/RDA. The results will be evaluated in the context of demanding science applications in partnership with solar physics, atmospheric science, and ecology researchers.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering.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.
美国国家科学基金会支持的Daniel K. Inouye太阳望远镜(DKIST)、国家大气研究中心(NCAR)和国家生态观测站网络(NEON)等科学设施收集了大量有关我们生活的世界的宝贵数据。这些数据可以用于科学和社会效益:在太阳的行为和磁场以及我们不断变化的环境方面取得突破性发现;提高强风暴和破坏性野火预报的速度和准确性;预测太阳耀斑对电力系统的破坏;还有很多其他的目的。在这些重要的科学数据得到有效利用之前,它们必须迅速、有效和可靠地交付给需要它们的人。研究人员需要交互式社区访问位于大数据档案中的难以获取的数据。由于NCAR、DKIST和NEON数据档案无法切实提供所有分析所需的计算资源,终端用户科学家需要能够定义、导航、下载和分析数据子集。目前基于网络的工具还不能胜任这些任务。NSF设施项目的虚拟数据集服务将通过开发组织、包装和快速传输数据的新方法来解决这一挑战。一个关键的创新将是开发用于定义、共享和操作“虚拟数据集”的方法,这些数据集是为特定目的从大量科学设施中“动态”提取的数据集合。研究人员可以定义虚拟数据集,就像购物者在网上“购物车”中组装产品一样。一旦定义,虚拟数据集就可以转移到远程计算机上进行分析,与同事共享,或为未来的项目扩展。我们将开发新的服务来(a)实现对科学设施的千万亿级数据档案中的虚拟数据集的定义、导航和选择性访问,以及(b)确保千万亿级数据档案和其他位置之间的整个数据集或数据子集的可靠、自动化、高效和安全复制和访问,包括最终用户计算机和旨在加速社区成员数据访问的远程镜像。这些新服务将建立在Globus平台之上,该平台已经在NCAR的研究数据档案(RDA)和许多其他研究数据中心大量使用。最终目标是与DKIST、NEON和NCAR/RDA合作,将新服务集成到操作系统中。结果将与太阳物理学、大气科学和生态学研究人员合作,在要求苛刻的科学应用背景下进行评估。该项目由计算机和信息科学与工程理事会的高级网络基础设施办公室支持。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。

项目成果

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Ian Foster其他文献

GreenFaaS: Maximizing Energy Efficiency of HPC Workloads with FaaS
GreenFaaS:利用 FaaS 最大限度提高 HPC 工作负载的能源效率
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alok V. Kamatar;Valerie Hayot;Y. Babuji;André Bauer;Gourav Rattihalli;Ninad Hogade;D. Milojicic;Kyle Chard;Ian Foster
  • 通讯作者:
    Ian Foster
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
DeepSpeed4Science 计划:通过复杂的人工智能系统技术实现大规模科学发现
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    S. Song;Bonnie Kruft;Minjia Zhang;Conglong Li;Shiyang Chen;Chengming Zhang;Masahiro Tanaka;Xiaoxia Wu;Jeff Rasley;A. A. Awan;Connor Holmes;Martin Cai;Adam Ghanem;Zhongzhu Zhou;Yuxiong He;Christopher Bishop;Max Welling;Tie;Christian Bodnar;Johannes Brandsetter;W. Bruinsma;Chan Cao;Yuan Chen;Peggy Dai;P. Garvan;Liang He;E. Heider;Pipi Hu;Peiran Jin;Fusong Ju;Yatao Li;Chang Liu;Renqian Luo;Qilong Meng;Frank Noé;Tao Qin;Janwei Zhu;Bin Shao;Yu Shi;Wen;Gregor Simm;Megan Stanley;Lixin Sun;Yue Wang;Tong Wang;Zun Wang;Lijun Wu;Yingce Xia;Leo Xia;Shufang Xie;Shuxin Zheng;Jianwei Zhu;Pete Luferenko;Divya Kumar;Jonathan Weyn;Ruixiong Zhang;Sylwester Klocek;V. Vragov;Mohammed Alquraishi;Gustaf Ahdritz;C. Floristean;Cristina Negri;R. Kotamarthi;V. Vishwanath;Arvind Ramanathan;Sam Foreman;Kyle Hippe;T. Arcomano;R. Maulik;Max Zvyagin;Alexander Brace;Bin Zhang;Cindy Orozco Bohorquez;Austin R. Clyde;B. Kale;Danilo Perez;Heng Ma;Carla M. Mann;Michael Irvin;J. G. Pauloski;Logan Ward;Valerie Hayot;M. Emani;Zhen Xie;Diangen Lin;Maulik Shukla;Thomas Gibbs;Ian Foster;James J. Davis;M. Papka;Thomas Brettin;Prasanna Balaprakash;Gina Tourassi;John P. Gounley;Heidi Hanson;T. Potok;Massimiliano Lupo Pasini;Kate Evans;Dan Lu;D. Lunga;Junqi Yin;Sajal Dash;Feiyi Wang;M. Shankar;Isaac Lyngaas;Xiao Wang;Guojing Cong;Peifeng Zhang;Ming Fan;Siyan Liu;A. Hoisie;Shinjae Yoo;Yihui Ren;William Tang;K. Felker;Alexey Svyatkovskiy;Hang Liu;Ashwin Aji;Angela Dalton;Michael Schulte;Karl Schulz;Yuntian Deng;Weili Nie;Josh Romero;Christian Dallago;Arash Vahdat;Chaowei Xiao;Anima Anandkumar;R. Stevens
  • 通讯作者:
    R. Stevens
An optical microscopy system for 3 D dynamic imagingRandy
用于 3D 动态成像的光学显微镜系统Randy
  • DOI:
  • 发表时间:
    2007
  • 期刊:
  • 影响因子:
    0
  • 作者:
    R. Hudson;John N. Aarsvold;Chin;Jie Chen;Peter Davies;T. Disz;Ian Foster;Melvin Griem;Man K Kwong;B. Lin
  • 通讯作者:
    B. Lin
Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept
化学与材料科学低成本自动驾驶实验室综述:“节俭双胞胎”概念
  • DOI:
    10.1039/d3dd00223c
  • 发表时间:
    2024-05-15
  • 期刊:
  • 影响因子:
    5.600
  • 作者:
    Stanley Lo;Sterling G. Baird;Joshua Schrier;Ben Blaiszik;Nessa Carson;Ian Foster;Andrés Aguilar-Granda;Sergei V. Kalinin;Benji Maruyama;Maria Politi;Helen Tran;Taylor D. Sparks;Alán Aspuru-Guzik
  • 通讯作者:
    Alán Aspuru-Guzik
Exploring Benchmarks for Self-Driving Labs using Color Matching
使用颜色匹配探索自动驾驶实验室的基准
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Tobias Ginsburg;Kyle Hippe;Ryan Lewis;Aileen Cleary;D. Ozgulbas;Rory Butler;Casey Stone;Abraham Stroka;Rafael Vescovi;Ian Foster
  • 通讯作者:
    Ian Foster

Ian Foster的其他文献

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{{ truncateString('Ian Foster', 18)}}的其他基金

Collaborative Research: NSF Workshop on Automated, Programmable and Self Driving Labs
合作研究:NSF 自动化、可编程和自动驾驶实验室研讨会
  • 批准号:
    2335910
  • 财政年份:
    2023
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
Frameworks: Garden: A FAIR Framework for Publishing and Applying AI Models for Translational Research in Science, Engineering, Education, and Industry
框架:Garden:用于发布和应用人工智能模型进行科学、工程、教育和工业转化研究的公平框架
  • 批准号:
    2209892
  • 财政年份:
    2022
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
Collaborative Research: OAC Core: ScaDL: New Approaches to Scaling Deep Learning for Science Applications on Supercomputers
协作研究:OAC 核心:ScaDL:在超级计算机上扩展深度学习科学应用的新方法
  • 批准号:
    2107511
  • 财政年份:
    2021
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
NSF Convergence Accelerator Track D: The Data Hypervisor: Orchestrating Data and Models
NSF 融合加速器轨道 D:数据管理程序:编排数据和模型
  • 批准号:
    2040718
  • 财政年份:
    2020
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
Collaborative Research: Frameworks: funcX: A Function Execution Service for Portability and Performance
协作研究:框架:funcX:可移植性和性能的函数执行服务
  • 批准号:
    2004894
  • 财政年份:
    2020
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
Framework: Software: HDR Globus Automate: A Distributed Research Automation Platform
框架:软件:HDR Globus Automate:分布式研究自动化平台
  • 批准号:
    1835890
  • 财政年份:
    2018
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
EAGER: Designing the OSN Software Platform
EAGER:设计 OSN 软件平台
  • 批准号:
    1836357
  • 财政年份:
    2018
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate
BD 辐条:辐条:中西部:协作:集成材料设计 (IMaD):利用、创新和传播
  • 批准号:
    1636950
  • 财政年份:
    2017
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
Collaborative Research: CyberSEES:Type 2: Framework to Advance Climate, Economics, and Impact Investigations with Information Technology (FACE-IT)
合作研究:Cyber​​SEES:类型 2:利用信息技术推进气候、经济和影响调查的框架 (FACE-IT)
  • 批准号:
    1331922
  • 财政年份:
    2013
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
Collaborative Research: Managing Cloud Usage Allocation and Accounting for the NSF Community
协作研究:管理 NSF 社区的云使用分配和核算
  • 批准号:
    1250555
  • 财政年份:
    2012
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant

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