MRI: Acquisition of CASTOR: A High-Performance Communication and Storage Backbone for Data-Intensive Science and Engineering Computing
MRI:收购 CASTOR:用于数据密集型科学和工程计算的高性能通信和存储骨干
基本信息
- 批准号:0421200
- 负责人:
- 金额:$ 60万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2004
- 资助国家:美国
- 起止时间:2004-09-01 至 2008-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This project, adding high performance interconnections and true data-intensive storage facilities, involves a high performance communication and storage backbone, named CASTOR. With the interconnections, this backbone enables achievement of the full benefits of the grid strategy to support data-intensive research in diverse disciplines. Major campus facilities will be linked by a 10Gigabit/s network and supported by 24 Terabyte storage systems. CASTOR's high performance links are well matched to those of Florida Lambda Rail and National Lambda Rail. Serving the following research domains, High Energy Physics, Chemical Physics and Material Science, Coastal and Estuarine Modeling, Medical Physics, and Computational Biology, the new infrastructure will also serve Computer Science and Engineering on data-intensive, high-performance grids. CASTOR is expected to strengthen the collaboration between multidisciplinary teams across the institution and provide a unique resource for the projects in which the researchers participate. The infrastructure provides a unique resource for applications that require storing, processing, and communicating very large-scale data sets at very high speeds. Reflecting real-world scenarios, the work involves the following technologies:* Grid Computing,* Distributed Storage, and* Advanced Networking.The first involves the development innovative middleware techniques that complement and extend virtualization of physical information resources to hide infrastructure heterogeneity and allow applications to execute on grid-enabled resources with user control and interaction from various web-based interfaces. The second provides a trial version of the grid environment to operate queries, providing a proof-of-concept for deployment in larger systems. CASTOR serves as an "ideal sandbox" to develop and test algorithms suitable for a distributed data pipeline. The last enables experiments in a research network operating at ultra-high speeds and multiple domains, hence contributes to enrich projects.
该项目增加了高性能互连和真正的数据密集型存储设施,涉及一个名为CASTOR的高性能通信和存储骨干。通过互连,这个主干能够实现网格战略的全部好处,以支持不同学科的数据密集型研究。主要园区设施将由10千兆位/秒网络连接,并由24 TB存储系统支持。卡斯特的高性能链接很好地匹配那些佛罗里达拉姆达铁路和国家拉姆达铁路。服务于以下研究领域,高能物理,化学物理和材料科学,海岸和河口建模,医学物理和计算生物学,新的基础设施还将服务于数据密集型,高性能网格的计算机科学和工程。预计CASTOR将加强整个机构多学科团队之间的合作,并为研究人员参与的项目提供独特的资源。基础设施为需要以非常高的速度存储、处理和通信非常大规模数据集的应用程序提供了独特的资源。这项工作反映了现实世界的情况,涉及以下技术:* 网格计算,* 分布式存储,* 高级网络。第一个涉及开发创新的中间件技术,补充和扩展物理信息资源的虚拟化,以隐藏基础设施的异构性,并允许应用程序在网格资源上执行,用户可以通过各种基于Web的界面进行控制和交互。第二个版本提供了一个网格环境的试用版来操作查询,为在更大的系统中进行部署提供了概念验证。CASTOR作为一个“理想的沙箱”来开发和测试适合分布式数据管道的算法。最后一个可以在超高速和多域的研究网络中进行实验,从而有助于丰富项目。
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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Sanjay Ranka其他文献
The Temporal Relationship Between Ecological Pain and Life-Space Mobility in Older Adults With Knee Osteoarthritis: A Smartwatch-Based Demonstration Study (Preprint)
患有膝骨关节炎的老年人的生态疼痛与生活空间流动性之间的时间关系:基于智能手表的演示研究(预印本)
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
M. Mardini;Subhash Nerella;Matin Kheirkhahan;Sanjay Ranka;R. Fillingim;Yujie Hu;D. Corbett;Erta Cenko;E. Weber;Parisa Rashidi;T. Manini - 通讯作者:
T. Manini
A comparison of different message-passing paradigms for the parallelization of two irregular applications
- DOI:
10.1007/bf00128099 - 发表时间:
1996-01-01 - 期刊:
- 影响因子:2.700
- 作者:
Seungjo Bae;Sanjay Ranka - 通讯作者:
Sanjay Ranka
Hybrid Approaches for Data Reduction of Spatiotemporal Scientific Applications
时空科学应用数据缩减的混合方法
- DOI:
10.1109/dcc58796.2024.00084 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Xiao Li;Qian Gong;Jaemoon Lee;S. Klasky;A. Rangarajan;Sanjay Ranka - 通讯作者:
Sanjay Ranka
Gene expression Markers improve clustering of CGH data
基因表达标记改善 CGH 数据的聚类
- DOI:
- 发表时间:
2006 - 期刊:
- 影响因子:0
- 作者:
Jun Liu;Sanjay Ranka;Tamer Kahveci - 通讯作者:
Tamer Kahveci
Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
保留导出量的误差有限的学习科学数据压缩
- DOI:
10.3390/app12136718 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Jaemoon Lee;Qian Gong;J. Choi;Tania Banerjee;S. Klasky;Sanjay Ranka;A. Rangarajan - 通讯作者:
A. Rangarajan
Sanjay Ranka的其他文献
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{{ truncateString('Sanjay Ranka', 18)}}的其他基金
SCC: Video Based Machine Learning for Smart Traffic Analysis and Management
SCC:基于视频的机器学习,用于智能流量分析和管理
- 批准号:
1922782 - 财政年份:2019
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
EAGER: Software-Hardware Co-Design Approaches for Multi-Level Memories
EAGER:多级存储器的软硬件协同设计方法
- 批准号:
1748652 - 财政年份:2017
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
CSR: Medium: Collaborative Research: SparseKaffe: high-performance, auto-tuned, energy-aware algorithms for sparse direct methods on modern heterogeneous architectures
CSR:媒介:协作研究:SparseKaffe:现代异构架构上稀疏直接方法的高性能、自动调整、能量感知算法
- 批准号:
1514116 - 财政年份:2015
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant
Student Travel Sponsorship for Third ACM BCB Conference, 2012
2012 年第三届 ACM BCB 会议学生旅行赞助
- 批准号:
1244794 - 财政年份:2012
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
Sparse Direct Methods on High-Performance Heterogeneous Architectures
高性能异构架构的稀疏直接方法
- 批准号:
1115297 - 财政年份:2011
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
CSR: Medium: Collaborative Research: GridPac: A Resource Management System for Energy and Performance Optimization on Computational Grids
CSR:媒介:协作研究:GridPac:计算网格能源和性能优化的资源管理系统
- 批准号:
0905308 - 财政年份:2009
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant
MCDA: Collaborative Research: A Multi-Element and Multi-Objective Optimization Approach for Allocating tasks to Multi-Core Processors
MCDA:协作研究:一种将任务分配给多核处理器的多元素和多目标优化方法
- 批准号:
0903430 - 财政年份:2009
- 资助金额:
$ 60万 - 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:
0325459 - 财政年份:2003
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant
CISE Educational Innovation Program: Mainstreaming Parallel and Distributed Computing in the Computer Science Undergraduate Curriculum
CISE 教育创新计划:将并行和分布式计算纳入计算机科学本科课程的主流
- 批准号:
9634470 - 财政年份:1996
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
Performance Modeling of SIMD and MIMD Parallel Computers using Neural Networks
使用神经网络对 SIMD 和 MIMD 并行计算机进行性能建模
- 批准号:
9110812 - 财政年份:1991
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant
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