MRI: Acquisition of a High Performance Computing Cluster to Support Multidisciplinary Big Data Analysis and Modeling

MRI：收购高性能计算集群以支持多学科大数据分析和建模

• 批准号: 1429518
• 负责人: Long-zhuang Li
• 金额: $ 39.9万
• 依托单位: Texas A&M University Corpus Christi
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1429518&HistoricalAwards=false
• 关键词: MRI; Acquisition; Performance; Computing; Cluster

This project, acquiring a High Performance Computing (HPC) cluster--the Corpus Christi High Performance cluster (CCHP)--supports large-scale data analysis and modeling research and research training across a broad variety of science and engineering technology disciplines in coastal and environmental studies. The CCHP cluster enables research projects ranging from computer science, life science, geographical information systems (GIS), remote sensing, to atmospheric science. These include:- Higher order tensor decomposition for big data,- Population Genomics of non-model species,- Geospatial crowdsourcing for natural disaster response,- Large-scale analytics of airborne and satellite remote sensing data and UAS imagery,- Global weather and climate analysis.CCHP contains compute nodes, GPU (graphical processing unit) nodes, and shared network storage, connected through InfiniBand switches in a fat tree topology to support high bandwidth low latency data communication (critical for HPC applications) while providing massive parallel computation on graphics processor cores. Moreover, the on-board Gigabit Ethernet ports with switch connection can support large data sets transmission which enables research in real time simulation and modeling. CCHP enables exploring parallel processing to process real large data sets in the higher order tensor decomposition, which is a basis for many data mining tasks including clustering, trend detection, and anomaly detection. This cluster is a necessary computational tool for biologists to use statistics effectively to realize the promise and power of societally relevant hypotheses in massively parallel nucleotide sequencing. The GIS researchers can utilize the CCHP cluster to advance geospatial crowdsourcing solution in case of natural disaster by providing quicker and more accurate geometric information. The cluster also enables remote sensing scientists to identify the relationship between environmental conditions, including land cover and use and rates of freshwater inflow, and attack the problems caused by the low accuracy of acquired unmanned aerial systems (UAS) images for precision agriculture. Furthermore, processing much longer time series of the global model and satellite data record, atmospheric scientists will be able to easily expand their research projects from regional to global scale.The impact of the CCHP cluster will be felt in many domains, especially on coastal and environmental studies that impacts society in general, impacting weather and climate model reliability and prediction skills in ecology, evolution, fisheries, conservation, and genetics. Moreover, curriculum materials obtained from the projects serviced by the instrumentation will enhance education and student learning at all levels, including K-12. These will impact existing courses and contribute to the creation of new ones.

该项目获得了高性能计算（HPC）群集 - Corpus Christi高性能集群（CCHP） - 支持大规模的数据分析，并在沿海和环境研究的各种科学和工程技术学科中进行大规模的研究和研究培训。 CCHP群集可实现研究项目，从计算机科学，生命科学，地理信息系统（GIS），遥感到大气科学。 These include:- Higher order tensor decomposition for big data,- Population Genomics of non-model species,- Geospatial crowdsourcing for natural disaster response,- Large-scale analytics of airborne and satellite remote sensing data and UAS imagery,- Global weather and climate analysis.CCHP contains compute nodes, GPU (graphical processing unit) nodes, and shared network storage, connected through InfiniBand switches in a fat树木拓扑支持高带宽低潜伏数据通信（对HPC应用程序至关重要），同时在图形处理器核心上提供大量的并行计算。此外，具有开关连接的板载千兆以太网端口可以支持大型数据集传输，从而可以实时模拟和建模进行研究。 CCHP使探索并行处理能够以高阶张量分解处理真实的大数据集，这是许多数据挖掘任务的基础，包括聚类，趋势检测和异常检测。该群集是生物学家有效地使用统计数据来实现社会相关假设在大规模平行核苷酸测序中的希望和力量的必要计算工具。 GIS研究人员可以通过提供更快，更准确的几何信息来利用CCHP群集来推动地理空间众包解决方案。该集群还使遥感科学家能够确定环境条件之间的关系，包括土地覆盖率和淡水流入率，并攻击因精确农业而获得的无人驾驶空中系统（UAS）图像的准确性较低而引起的问题。此外，处理全球模型和卫星数据记录的时间序列更长的时间序列，大气科学家将能够轻松地将其研究项目从区域扩展到全球规模。在许多领域中，CCHP群集的影响将在许多领域中感受到，尤其是在沿海和环境研究中，尤其是对社会的影响，从而影响社会，从而影响整个社会，从而影响天气的可靠性和气候模型和预测能力，生态学，进化，生态学，生态学，生态学，乡亲，基因，以及基因，以及cent and centick and cent and gent and gent和genit。此外，从仪器服务的项目中获得的课程材料将增强包括K-12在内的各个级别的教育和学生学习。这些将影响现有课程，并有助于创建新课程。