MRI: Acquisition of an Infrastructure for Prototyping Next-Generation Algorithms for Large-Scale Visualization, Data Processing and Analysis

MRI：采购用于大规模可视化、数据处理和分析的下一代算法原型设计的基础设施

• 批准号: 1229185
• 负责人: Claudio Silva
• 金额: $ 80万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1229185&HistoricalAwards=false
• 关键词: MRI; Acquisition; Infrastructure; Prototyping; Next

Proposal #: 12-29185PI(s): Silva, Claudio; Freire, Juliana; Iacono, John; Suel, Torsten; Vo, HuyInstitution: Polytechnic University of New YorkTitle: MRI/Acq.: Prototyping Next-Generation Algorithms for Large-scale Visualization, Data Processing and AnalysisProject Proposed:This project, acquiring an instrument for prototyping the next-generation algorithms for large-scale visualization, data processing and analysis, facilitates the development and evaluation of next-generation algorithms and systems that leverage different hardware configurations. It consists of computational resources in the form of an SGI UV 1000 machine with 384 cores and four Tesla M2070 GPUs, and a small cluster composed of 32 nodes (384 cores total), each with two Tesla M2070 GPUs; and disk resources in the form of solid-state storage cards and an Isilon Storage Cluster. The instrumentation supports projects in various domains. The computer science projects include - Dataflow architectures, - Theoretical models of computation, - Web search, - Visualization, and - Data mining; While the application area research projects (undertaken with external collaborators) relate to- Wildfire simulation, - Bird migration simulation, and - Climate science. Broader Impacts: The team plans to release the software developed as part of this project as open source, which carries potential for significant broader impact. The involvement of graduate and undergraduate students in the project provides mentoring opportunities.

提案#:12-29185PI(s): Silva, Claudio；Freire,朱莉安娜;Iacono,约翰;Suel Torsten;Vo， huy毕业院校：纽约理工大学职称：MRI/Acq项目建议：该项目将获得用于大规模可视化、数据处理和分析的下一代算法原型的仪器，促进利用不同硬件配置的下一代算法和系统的开发和评估。它由一台384核的SGI UV 1000机器和4颗Tesla M2070 gpu组成的计算资源，以及一个由32个节点（共384核）组成的小集群，每个节点有2颗Tesla M2070 gpu；和磁盘资源，以固态存储卡和硅存储集群的形式。该工具支持各种领域中的项目。计算机科学项目包括数据流体系结构、计算理论模型、网络搜索、可视化和数据挖掘；而应用领域的研究项目（与外部合作者进行）涉及野火模拟，鸟类迁徙模拟和气候科学。更广泛的影响：团队计划将作为该项目的一部分开发的软件作为开放源代码发布，这可能会产生更广泛的影响。研究生和本科生在项目中的参与提供了指导机会。

项目成果

GPU Rasterization for Real-Time Spatial Aggregation over Arbitrary Polygons

• DOI: 10.14778/3157794.3157803
• 发表时间: 2017-11
• 期刊: Proc. VLDB Endow.
• 作者: Eleni Tzirita Zacharatou;Harish Doraiswamy;A. Ailamaki;Cláudio T. Silva;J. Freire