The procurement of a high end computer (HEC) resource is proposed to meet demands of a broad range of 33 research groups at the University of Minnesota that are supported by 91 currently funded NIH grants, including 47 R01 grants. This proposal is driven by the growing need to tackle high- impact problems that involve the acquisition, analysis and visualization of petascale data from high performance computing and high-throughput technologies. These tremendously memory and disk/IO intensive biomedical applications require a specially designed total hardware solution. Four major user groups have been identified that have urgent demands for this enabling technology: 1) multi- scale modeling, 2) chemical dynamics, 3) bioinformatics and computational biology, and 4) biomedical imaging. The HEC solution is based on a 1,152-core/2.3TB SMP SGI Ultraviolet (UV) server and Virtu VN200 visualization nodes with Infinite Storage 4600 disk arrays having 32 TB fibre-channel, 512 TB SATA capacity and CXFS file system. This integrated total HEC solution is a unique, state of the art system that has very large shared memory and expansive, ultrafast data storage and transfer capabilities to meet the specific needs of these four major user groups, and will fill a critical niche in the University of Minnesota's computational support infrastructure centralized at the Minnesota Supercomputing Institute (MSI). The proposed system is significantly different from the MSI's current core hardware, and will greatly extend the range of NIH-supported biomedical applications that can be served by the MSI. The MSI is committed to house, power and administer the system, and will provide training and software support, as well as develop a custom queuing system to streamline throughput for major users. The proposed HEC solution will provide on-demand access to a broad community of NIH-supported researchers at the University of Minnesota through both direct access and novel caGrid web services. The caGrid services developed at the University of Minnesota and the Mayo Clinic as well as standard caGrid services will be deployed on the HEC, and will support the data-intensive and memory-intensive needs of NIH researchers using core laboratory services. The result will be that throughput of calculations with intensive memory/disk requirements will be greatly enhanced, human time involved in the transfer, manipulation, analysis and visualization of very large data sets will be significantly reduced, and new applications that were impeded by resource limitations will be enabled. The proposed HEC solution is expected to create a wealth of new job opportunities, facilitate breakthroughs in biomedical research and have significant impact on human health.
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