MRI: Acquisition of a High Performance Computer Cluster Supporting Computational Science Research and Learning

MRI：获取支持计算科学研究和学习的高性能计算机集群

• 批准号: 0922644
• 负责人: Stefan Robila
• 金额: $ 19万
• 依托单位: Montclair State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0922644&HistoricalAwards=false
• 关键词: MRI; Acquisition; Performance; Computer; Cluster

Proposal #: CNS 09-22644PI(s): Robila, Stefan A.Institution: Montclair State University Title: MRI/Acq.: High Performance Computer Cluster Supporting Computational Science Research and LearningProject Proposed:This project, acquiring a high performance computer cluster, aims to expand interdisciplinary projects in three departments (CS, Math Science, and Linguistics) and enable collaboration with a research intensive university (Syracuse). These projects require significant computation power and describe problems where the size of the data sets continues to grow. The work involves novel approaches for parallel image and signal processing, and in particular spectral imaging. The approach includes design of parallel algorithms and the development of theories on how to parallelize the general data processing steps. Other projects use parallel processing for phylogenetic modeling, use dynamic systems for disease modeling, parallelization techniques for the use of scattering theory, and the cross-lingual morphosyntactic tagging.Broader Impacts: This project helps to attract more students to hands-on multidisciplinary research and to establish and strengthen a cross-institutional research partnership between an undergraduate-serving teaching institution and a research university. The research on spectral imaging parallel applications allows for the use of spectral sensors in new applications improving the timeliness and accuracy of results. In terms of dynamic systems, computer modeling and computational analysis of disease dynamics might contribute to determine the best policy for a given epidemic situation. Applications and contributions of scattering theory to science include deep earth seismology, exploration of underground resources, engineering, mine detection, and other military applications. In phylogeny, the ability to reconstruct optimal evolutionary trees based on objective criteria impacts directly the understanding of the relationships among organisms, human evolution, and spread of infectious disease.

提案编号：CNS 09-22644PI(s): Robila, Stefan a .机构：Montclair State University头衔：MRI/Acq。项目建议：该项目收购一个高性能计算机集群，旨在扩展三个系（计算机科学、数学科学和语言学）的跨学科项目，并与一所研究型大学（锡拉丘兹大学）开展合作。这些项目需要大量的计算能力，并描述数据集规模持续增长的问题。这项工作涉及到并行图像和信号处理的新方法，特别是光谱成像。该方法包括并行算法的设计和如何并行化一般数据处理步骤的理论发展。其他项目使用并行处理进行系统发育建模，使用动态系统进行疾病建模，使用散射理论的并行化技术，以及跨语言形态句法标记。更广泛的影响：本计划有助吸引更多学生参与多学科研究，并建立和加强本科教学机构与研究型大学之间的跨机构研究伙伴关系。光谱成像并行应用的研究允许在新的应用中使用光谱传感器，提高结果的及时性和准确性。就动态系统而言，计算机建模和疾病动力学的计算分析可能有助于确定针对特定流行病情况的最佳政策。散射理论的科学应用和贡献包括深地地震学、地下资源勘探、工程、地雷探测和其他军事应用。在系统发育学中，基于客观标准重建最优进化树的能力直接影响到对生物之间关系、人类进化和传染病传播的理解。