{"response":{"award":[{"abstractText":"We will acquire a graphical processing unit (GPU)-based 32-node parallel high-performance computer in support of science and engineering research at the University of New Mexico (UNM). The machine will be housed at the UNM Center for Advanced Research Computing (CARC), a campus-wide shared facility. The machine will significantly expand the capabilities of the campus supercomputer center (CARC), increasing total available compute cycles from 5 to 41 TFlops peak, and corresponding online storage capacity from 23 to 55 TB. Configured as a tightly-coupled cluster of \"fat nodes\", the new machine and associated software will enable researchers to run calculations as intrinsically parallel (multi-threaded, 10-50x GPU-accelerated) single-node, shared-memory jobs, with further scalability achievable through multi-node message-passing parallelism.\r\n\r\nThe seventeen collaborating faculty on this proposal span nine departments and three Colleges. This new system will support research in nano-bio-materials science, including molecular biophysics, chemical and condensed matter physics, materials physics, mathematical biology, molecular biology, catalysis, novel sensor materials, and structural materials; advanced graphics, image processing and visualization, including biophysical imaging using quantum dots, 3D animation and rendering, fMRI image analysis and 3D computed tomography; and geophysics, including computational electromagnetics and geological modeling. \r\n\r\nIt will also provide the opportunity for students and researchers to remain on the computing technology curve and gain early access to a transformative next- generation architecture. Outreach and training classes will be provided by CARC technical staff to significantly expand the user base, particularly to 'nano-bio' researchers (students and faculty) associated with the two NSF IGERT training programs at UNM, Nanoscience and Microsystems and Integrating Nanotechnology with Cell Biology and Neuroscience, as well as the Initiatives to Maximize Student Diversity (IMSD) program. IMSD is aimed at increasing the number of under-represented minorities in biomedical research and has students from Biology, Biochemistry, Chemistry, Computer Science, Computer Engineering, Chemical Engineering and Psychology. Students and researchers from the MIND Research Network (neuroscience) and the NSF-funded Data Observation Network for Earth (DataOne) project (environmental sciences) will also have access to and benefit from the machine. \r\n\r\nAs an EPSCoR and Minority-Serving Institution, UNM has a unique demographic makeup: it is the only Carnegie Research/Doctoral Extensive institution in the U.S. that is also a Hispanic Serving Institution, with 32% Hispanic, 6% Native American and 3% African American students. The availability of this next-generation supercomputing architecture as a shared, campus-wide resource will provide an exceptional opportunity to engage students, including many from traditionally underrepresented minorities, at the forefront of computational science and engineering research.","activeAwd":"false","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF NEW MEXICO","awardeeAddress":"1 UNIVERSITY OF NEW MEXICO","awardeeCity":"ALBUQUERQUE","awardeeCountryCode":"US","awardeeDistrict":"01","awardeeDistrictCode":"NM01","awardeeName":"University of New Mexico","awardeePhone":"5052774186","awardeeStateCode":"NM","awardeeZipCode":"871310001","cfdaNumber":"47.070","coPDPI":["Hua Guo hguo@unm.edu","Timothy L Thomas thomas@phys.unm.edu","Lydia E Tapia tapia@cs.unm.edu","Pradeep Sen (Former) psen@ece.ucsb.edu","Jamesina J Simpson (Former) jamesina.simpson@utah.edu"],"date":"08/26/2010","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"435077","expDate":"08/31/2015","fundAgencyCode":"4900","fundProgramName":"Major Research Instrumentation","fundsObligated":["FY 2010 = $435,077.00"],"fundsObligatedAmt":"435077","histAwd":"false","id":"1040530","initAmendmentDate":"08/26/2010","latestAmendmentDate":"07/31/2013","managingPec":"118900","orgCodeDir":"05000000","orgCodeDiv":"05090000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure (OAC)","orgUrl":"https://www.nsf.gov/div/index.jsp?div=OAC","parentUeiNumber":"","pdPIName":"Susan R Atlas","perfAddress":"1 UNIVERSITY OF NEW MEXICO","perfCity":"ALBUQUERQUE","perfCountryCode":"US","perfDistrict":"01","perfDistrictCode":"NM01","perfLocation":"University of New Mexico","perfStateCode":"NM","perfZipCode":"871310001","pi":["Susan R Atlas susie@sapphire.phys.unm.edu"],"piEmail":"susie@sapphire.phys.unm.edu","piFirstName":"Susan","piId":"000235439","piLastName":"Atlas","piMiddeInitial":"R","poEmail":"edwalker@nsf.gov","poName":"Edward Walker","poPhone":"7032924863","primaryProgram":["01001011DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"118900","program":"MAJOR RESEARCH INSTRUMENTATION, EXP PROG TO STIM COMP RES","progRefCode":"1189, 9150","projectOutComesReport":"
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We often hear of computation described as the “third leg of science”: it complements theoretical modeling and laboratory experiments to provide a virtual window into the complexities of the natural world.  Scientists and engineers use computer models to avoid the high cost of extended trial-and-error in the laboratory, and to explore aspects of the natural world that they cannot directly measure—such as the spontaneous synthesis of chemical elements in an exploding star, or the propagation patterns of a tsunami.  Computers help us build faster, more aerodynamic race cars and stronger, lighter airplanes, and engineer better bridges; they enable chemists to morph the structure of a molecule from the dirt of an alpine plateau into a powerful organ transplant anti-rejection drug; and help biologists identify DNA mutation patterns in a child’s blood cells so that doctors can design a tailored leukemia treatment.  These are all remarkable, real-life examples of the power of computing as an agent of science and engineering change and discovery.  Inevitably, though, the more powerful the computers, the more ambitious our scientific dreams:  there is always one more equation, one additional parameter, that will push the next iteration of a model beyond the limits of current computational capabilities. With this award, we have built a powerful computing environment—composed of tens of thousands of CPU and graphics processing unit (GPU) cores, and 5,000 times the disk capacity of the average laptop—to enable researchers at the University of New Mexico, together with a new generation of students, to tackle some of the most important and challenging problems at the frontiers of science and engineering.

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The system that we have deployed at the UNM supercomputer center (Center for Advanced Research Computing) with support from this award consists of two major components: a high-end array of hard drives (four refrigerator-racks’ worth) for storing data (files, images, results of calculations, and data collected from satellites and field surveys, or aggregated from databases around the world); and three supercomputers (20 racks total)—Metropolis, Ulam, and Xena—the first two named for Los Alamos scientists, and the third for a fictional warrior princess.  Ulam and Metropolis are former Department of Energy systems gifted to UNM with support from a separate NSF award to the New Mexico Consortium,  a non-profit formed by the state’s three research universities.  Xena, purchased under this award, has 576 CPU cores, NVIDIA GPUs similar to those used in gaming systems, and four ‘big memory’ nodes, each with 250-750x the RAM of a typical PC.  The GPUs enable researchers to dramatically accelerate their calculations, and the big memory nodes make it possible to tackle modeling problems that require the analysis of exceptionally large datasets, or searches over enormous parameter spaces.  Over the course of the five years of this award, researchers from 20 departments and four Colleges have used these supercomputers, together with the research storage system (expanded four times; currently at 1.5 petabytes in size) leading to more than 200 technical publications.

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In the end, one of the most important broader impacts that emerged from this project was entirely unexpected.  The timing of the award (2010) coincided with the dawn of the so-called ‘big data’ era, and the pervasive use of machine learning to discern patterns in data, in fields ranging from genomics and medical imaging to handwriting analysis and literary attribution.  Machine learning is an extraordinarily difficult problem; it requires access to very large collections of data in order to train the computer&rsquo...","publicAccessMandate":"0","startDate":"09/01/2010","title":"MRI: Acquisition of a GPU-Accelerated Parallel Supercomputer for Computational Science and Engineering Research at the University of New Mexico","transType":"Standard Grant","ueiNumber":"F6XLTRUQJEN4"}],"metadata":{"offset":0,"rpp":25,"totalCount":1}}}