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

Texas A&M University Corpus Christi, Corpus Christi TX

Investigators

Abstract

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.