PIRE: Training and Workshops in Data Intensive Computing Using The Open Science Data Cloud
PIRE:使用开放科学数据云进行数据密集型计算的培训和研讨会
基本信息
- 批准号:1129076
- 负责人:
- 金额:$ 348.95万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2010
- 资助国家:美国
- 起止时间:2010-12-03 至 2016-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Many scientists today face the unprecedented challenge of managing and analyzing a rapidly growing set of complex data. This international PIRE project aims to narrow the growing gap between the capability of modern scientific instruments to produce data and the ability of researchers to manage, analyze, and share those data in a reliable and timely manner. The emerging technology of cloud computing is a step forward from the current cyberinfrastructure. Cloud computing involves clusters (the "clouds") of distributed computers that provide potentially less expensive, more flexible, and more powerful on-demand resources and services over a network, usually the Internet, while providing the scale and the reliability of a data center. This PIRE team intends to help develop large-scale distributed computing capabilities - the Open Science Data Cloud (OSDC) - to provide long term persistent storage for scientific data and state-of-the-art services for integrating, analyzing, sharing and archiving scientific data. The group proposes to study and strengthen storage systems that integrate specialized network protocols and support data transport over wide-area, high-performance networks. As data grows in size, the only practical means to analyze it is to use parallel programming, but until recently it has been time consuming for a domain scientist to take advantage of parallel programming. Another research focus will be to develop new classes of cloud-based parallel programming frameworks and to integrate them into the cloud infrastructure so that this technology is more broadly available to scientists. In addition to the research dimensions of this project, another key aspect is the involvement, in workshops and in subsequent use of the cloud cyberinfrastructure, of many domain scientists and their students. These groups will be trained in the basics of cloud computing and then will work to ensure that the cloud computing research advances maximize the manageability and analytical power of the complex datasets unique to their disciplines. This PIRE project embraces cloud computing as a global issue and so taps the cloud computing, high performance networking, domain science, e-Science, education and outreach expertise of its many collaborators in Europe, Asia and South America. Foreign partners also provide a natural mechanism to engage international scientific datasets and distributed networks, and accommodate different international standards to guarantee interoperability. The international collaborators can provide an entry into international collaborations for U.S. graduate students and early career scientists and can also serve as global ambassadors for this new cyberinfrastructure, helping to garner widespread support that will be critical to its future adoption.The project will build a strong cadre of students with a global perspective on scientific data management in many research areas vital to U.S. and international scientific collaborations. The project will provide U.S. graduate students and early career scientists with international research and education experiences with leading scientists via research and training at foreign institutions and participation in annual workshops. As a group, the PIRE students will share an interest in data intensive computing but will be drawn from fields as diverse as computer science, physics, astronomy, geosciences, chemistry, engineering, and biology, lending an interdisciplinary vigor to their training. The PIRE team members will also develop 1-2 day and 1-2 week courses on data intensive computing, with hands-on exercises developed by U.S. and international faculty in computer science and the domain sciences.This PIRE project is likely to have numerous impacts above the level of the individual collaborators. For the U.S. PIRE institutions, it will strengthen current linkages and collaborations in the global Cloud Computing community and engage more U.S. students in international interdisciplinary research teams for the service, support and analysis of large scientific datasets. The project will enhance internationalizing efforts both at the University of Illinois at Chicago and at Florida International University by providing opportunities for short term research abroad and other academic experiences to a diverse group of students. This project will increase the virtual international engagement of the U.S. institutions via distributed research collaborations, courses with transcontinental participation, global web discussions, and focused social networking forums. Increasing the number of scientists with expertise in managing and analyzing very large datasets is also vital to the future of our nation. Finally, since this transformative technology is broadly applicable to any scientific project struggling to manage and analyze the volume of data produced, the OSDC and its facilitative impacts are likely to persist long after the PIRE project has ended. Participating U.S. institutions include the National Center for Data Mining (NCDM) at the University of Illinois at Chicago and Florida International University. OSDC U.S. partner institutions include the University of Chicago and Johns Hopkins University. Partnering foreign institutions include the University of Edinburgh (UK); Universidade Federal Fluminense (Brazil); University of Amsterdam (The Netherlands); National Institute of Advanced Industrial Science and Technology (AIST) (Japan); Korea Institute of Science and Technology Information (KISTI) Supercomputing Center; Beijing Institute of Genomics (BIG) - Chinese Academy of Sciences, and the State University of Sao Paulo (Brazil). This project is cofunded by the NSF's Office of International Science and Engineering, the Office of Cyberinfrastructure, the Division of Computer and Communication Foundations, the Division of Astronomical Sciences, and the Division of Physics.
如今,许多科学家面临着管理和分析快速增长的复杂数据集的前所未有的挑战。这个国际PIRE项目旨在缩小现代科学仪器产生数据的能力与研究人员以可靠和及时的方式管理,分析和共享这些数据的能力之间日益扩大的差距。新兴的云计算技术比当前的网络基础设施向前迈出了一步。 云计算涉及分布式计算机的集群(“云”),其通过网络(通常是因特网)提供潜在地更便宜、更灵活和更强大的按需资源和服务,同时提供数据中心的规模和可靠性。 该PIRE团队旨在帮助开发大规模分布式计算能力-开放科学数据云(OSDC)-为科学数据提供长期持久存储,并为集成,分析,共享和归档科学数据提供最先进的服务。 该小组建议研究和加强集成专用网络协议并支持广域高性能网络数据传输的存储系统。随着数据规模的增长,分析数据的唯一实用方法是使用并行编程,但直到最近,领域科学家利用并行编程一直很耗时。 另一个研究重点将是开发新的基于云的并行编程框架,并将其集成到云基础设施中,以便科学家更广泛地使用这项技术。 除了该项目的研究层面,另一个关键方面是参与研讨会和随后使用云网络基础设施,许多领域科学家及其学生。这些小组将接受云计算基础知识的培训,然后将努力确保云计算研究的进展最大限度地提高其学科独特的复杂数据集的分析能力。这个PIRE项目将云计算作为一个全球性问题,因此利用了欧洲、亚洲和南美洲许多合作者的云计算、高性能网络、领域科学、电子科学、教育和推广专业知识。 外国合作伙伴还提供了一个自然的机制,以参与国际科学数据集和分布式网络,并适应不同的国际标准,以保证互操作性。国际合作者可以为美国研究生和早期职业科学家提供进入国际合作的机会,也可以作为这一新网络基础设施的全球大使,该项目将培养一支强大的学生队伍,他们在对美国和国际至关重要的许多研究领域的科学数据管理方面具有全球视野。科学合作。该项目将通过在外国机构的研究和培训以及参加年度研讨会,为美国研究生和早期职业科学家提供与领先科学家的国际研究和教育经验。作为一个群体,PIRE学生将分享对数据密集型计算的兴趣,但将从计算机科学,物理学,天文学,地球科学,化学,工程和生物学等不同领域中汲取经验,为他们的培训提供跨学科的活力。PIRE团队成员还将开发1-2天和1-2周的数据密集型计算课程,并由美国和国际计算机科学和领域科学教师开发实践练习。该PIRE项目可能会产生超出个人合作者水平的许多影响。对于美国的PIRE机构来说,它将加强全球云计算社区目前的联系和合作,并吸引更多的美国学生加入国际跨学科研究团队,为大型科学数据集提供服务,支持和分析。该项目将通过向不同的学生群体提供短期国外研究和其他学术经验的机会,加强芝加哥伊利诺斯大学和佛罗里达国际大学的国际化努力。该项目将通过分布式研究合作、跨洲参与的课程、全球网络讨论和有针对性的社交网络论坛,增加美国机构的虚拟国际参与。 增加具有管理和分析超大型数据集专业知识的科学家数量对我们国家的未来也至关重要。 最后,由于这种变革性技术广泛适用于任何努力管理和分析所产生的数据量的科学项目,OSDC及其促进性影响可能会在PIRE项目结束后很长一段时间内持续存在。参与的美国机构包括位于芝加哥的伊利诺伊大学国家数据挖掘中心(NCDM)和佛罗里达国际大学。 OSDC的美国合作机构包括芝加哥大学和约翰霍普金斯大学。 合作的外国机构包括爱丁堡大学(英国)、弗鲁米嫩塞联邦大学(巴西)、阿姆斯特丹大学(荷兰);国家先进工业科学技术研究所(AIST)(日本);韩国科学技术信息研究所(KISTI)超级计算中心;中国科学院北京基因组研究所(BIG)和圣保罗州立大学(巴西)。该项目由NSF的国际科学与工程办公室,网络基础设施办公室,计算机和通信基础部门,天文科学部门和物理部门共同资助。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Robert Grossman其他文献
A graph model based study on regulatory impacts of transcription factors of <em>Drosophila melanogaster</em> and comparison across species
- DOI:
10.1016/j.bbrc.2009.06.055 - 发表时间:
2009-09-04 - 期刊:
- 影响因子:
- 作者:
Feng Tian;Jia Chen;Suying Bao;Lin Shi;Xiangjun Liu;Robert Grossman - 通讯作者:
Robert Grossman
Autologous HER2 CMV bispecific CAR T cells are safe and demonstrate clinical benefit for glioblastoma in a Phase I trial.
- DOI:
10.1186/2051-1426-3-s2-o11 - 发表时间:
2015-11-04 - 期刊:
- 影响因子:10.600
- 作者:
Nabil Ahmed;Vita Brawley;Meenakshi Hegde;Kevin Bielamowicz;Amanda Wakefield;Alexia Ghazi;Aidin Ashoori;Oumar Diouf;Claudia Gerken;Daniel Landi;Mamta Kalra;Zhongzhen Yi;Cliona Rooney;Gianpietro Dotti;Adrian Gee;Helen Heslop;Stephen Gottschalk;Suzanne Powell;Robert Grossman;Winfried Wels;Yzonne Kew;David Baskin;Jonathan Zhang;Pamela New;John Hicks - 通讯作者:
John Hicks
Hopf-algebraic structure of combinatorial objects and differential operators
- DOI:
10.1007/bf02764614 - 发表时间:
1990-02-01 - 期刊:
- 影响因子:0.800
- 作者:
Robert Grossman;Richard G. Larson - 通讯作者:
Richard G. Larson
A comparison of intensity modulated conformal therapy with a conventional external beam stereotactic radiosurgery system for the treatment of single and multiple intracranial lesions.
调强适形疗法与传统外束立体定向放射外科系统治疗单个和多个颅内病变的比较。
- DOI:
- 发表时间:
1996 - 期刊:
- 影响因子:0
- 作者:
S. Woo;W. Grant;D. Bellezza;Robert Grossman;P. Gildenberg;L. Carpenter;M. Carol;E. Butler - 通讯作者:
E. Butler
Anti-HLA-DP antibodies may represent a significant barrier to successful kidney transplantation in re-grafted patients
- DOI:
10.1016/j.humimm.2005.10.009 - 发表时间:
2005-08-01 - 期刊:
- 影响因子:
- 作者:
Malek Kamoun;Marty Sellers;Christa Whitney-Miller;Jane Kearns;Erin Pierce;John Tomaszewski;Alden Doyle;Robert Grossman;Roy Bloom;Ali Naji;James Markmann;Simin Goral - 通讯作者:
Simin Goral
Robert Grossman的其他文献
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{{ truncateString('Robert Grossman', 18)}}的其他基金
Workshop on Translational Data Science (TDS 17)
转化数据科学研讨会 (TDS 17)
- 批准号:
1742814 - 财政年份:2017
- 资助金额:
$ 348.95万 - 项目类别:
Standard Grant
BIGDATA: Small: DCM: Open Flow Enabled Hadoop over Local and Wide Area Clusters
BIGDATA:小型:DCM:本地和广域集群上支持开放流的 Hadoop
- 批准号:
1251201 - 财政年份:2013
- 资助金额:
$ 348.95万 - 项目类别:
Standard Grant
SDCI Net: UD* - A UDT-Based Application Suite for High Performance Data Transport
SDCI Net:UD* - 基于 UDT 的高性能数据传输应用程序套件
- 批准号:
1127316 - 财政年份:2011
- 资助金额:
$ 348.95万 - 项目类别:
Standard Grant
PIRE: Training and Workshops in Data Intensive Computing Using The Open Science Data Cloud
PIRE:使用开放科学数据云进行数据密集型计算的培训和研讨会
- 批准号:
0968341 - 财政年份:2010
- 资助金额:
$ 348.95万 - 项目类别:
Continuing Grant
Web-based Interactive Organic Chemistry Homework
基于网络的互动有机化学作业
- 批准号:
0816783 - 财政年份:2009
- 资助金额:
$ 348.95万 - 项目类别:
Standard Grant
Web-based Interactive Organic Chemistry Homework
基于网络的互动有机化学作业
- 批准号:
0441201 - 财政年份:2005
- 资助金额:
$ 348.95万 - 项目类别:
Standard Grant
MRI: International Data Mining Grid Testbed for Research in High Performance Data Transport, Data Integration, and Data Exploration -- Instrument Development Proposal
MRI:用于高性能数据传输、数据集成和数据探索研究的国际数据挖掘网格测试平台——仪器开发提案
- 批准号:
0420847 - 财政年份:2004
- 资助金额:
$ 348.95万 - 项目类别:
Standard Grant
Hyperbolic Classification and Regression Trees
双曲分类和回归树
- 批准号:
0442178 - 财政年份:2004
- 资助金额:
$ 348.95万 - 项目类别:
Standard Grant
SCI: II: The TeraFlow Project: High Performance Flows for Mining Large Distributed Data Archives
SCI:II:TeraFlow 项目:用于挖掘大型分布式数据档案的高性能流程
- 批准号:
0430781 - 财政年份:2004
- 资助金额:
$ 348.95万 - 项目类别:
Standard Grant
ITR: Collaborative Research: A Data Mining and Exploration Middleware for Grid and Distributed Computing
ITR:协作研究:用于网格和分布式计算的数据挖掘和探索中间件
- 批准号:
0325013 - 财政年份:2003
- 资助金额:
$ 348.95万 - 项目类别:
Continuing Grant
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