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Award Abstract #1228312

MRI: Acquisition of High Performance Computing Instrument for Collaborative Data-Enabled Science

NSF Org: CNS
Division Of Computer and Network Systems
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Initial Amendment Date: August 25, 2012
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Latest Amendment Date: December 12, 2014
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Award Number: 1228312
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Award Instrument: Standard Grant
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Program Manager: Rita V. Rodriguez
CNS Division Of Computer and Network Systems
CSE Direct For Computer & Info Scie & Enginr
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Start Date: September 1, 2012
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End Date: August 31, 2015 (Estimated)
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Awarded Amount to Date: $1,024,160.00
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Investigator(s): Jill Gemmill gemmill@clemson.edu (Principal Investigator)
Amy Apon (Former Principal Investigator)
Walter Ligon (Co-Principal Investigator)
Steven Stuart (Co-Principal Investigator)
Melissa Smith (Co-Principal Investigator)
Jill Gemmill (Former Co-Principal Investigator)
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Sponsor: Clemson University
300 BRACKETT HALL
CLEMSON, SC 29634-0001 (864)656-2424
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NSF Program(s): SPECIAL PROJECTS - CISE,
DATANET,
MAJOR RESEARCH INSTRUMENTATION
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Program Reference Code(s): 1189, 9150, 9251
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Program Element Code(s): 1714, 7726, 1189

ABSTRACT

Proposal #: 12-28312

PI(s): Apon, Amy W.

Gemmill, Jill; Ligon, Walter B.; Smith, Melissa C.; Stuart, Steven J.

Institution: Clemson University

Title: MRI/Acq.: High Performance Computing Instrument for Collaborative Data-Enabled Science

Project Proposed:

This project, acquiring a high performance computing (HPC) for collaborative data-enabled science, aims to enable fundamental and applied research in areas of experimental HPC, including scalable file systems, accelerator technologies, middleware architectures, and cluster scheduling policies. The instrument will be used as a production resource to support several compelling science applications in materials and biomolecular modeling, bioinformatics (and others) and will contribute a significant new component of the institution?s well established and managed Palmetto cluster while providing critically needed file storage, and accelerator systems not currently available. Supported projects include:

- Scalable file system implementation,

- Exploiting and analyzing concurrency in heterogeneous computing environments,

- High performance cluster modeling, analysis, and characterization,

- File replication and consistency maintenance,

- Cloud architecture,

- Properties of advanced materials and biomolecular systems,

- Molecular dynamic simulation studies of poly-dots, new light-emitting nanoparticles,

- Biomolecular evolution,

- Optimizing catalysts for direct chemical remediation from water,

- Protein-surface interactions,

- Map/Reduce framework for next generation sequencing data assembly,

- Searching for identification of significantly conserved protein blocks,

- Digital manufacturing,

- Statistical inference in nonparametrics models of production,

- Analysis of the structures of hospital and banking industries,

- Multiple scatter rendering,

- Digital scholarship, and

- Applications of computing to the geosciences.

Regular planned input from the scientific team is expected to facilitate the development of policies for use of the instrument as a test bed for experimental high performance computing systems research as well as a shared production resource for scientific applications.

Broader Impacts:

The instrument serves as a tool for the collaborative team that includes more than 20 faculty from 4 institutions, and more than 250 graduate and undergraduate students. The system will also be utilized by the already established ?Desktop to Teragrid? program that reaches more than a dozen universities across South Carolina, an EPSCoR state, and with regional partners. The proposal team includes 6 women senior personnel. The institution has outreach, education, and training activities, and has engaged SC?s only public HBCU, South Carolina State University, as well as other minority institutions within the region. Development of interdisciplinary curricula will support computational and data-enabled science and engineering.


PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Chen Lu, Jason Schwier, Ryan Craven, Yu Lu, R. R. Brooks and Christopher Griffin, Brooks. "A Normalized Statistical Metric Space for Markov Models," IEEE Transactions on Cybernetics, v.43, 2013, p. 806.

Yu Lu, Jason Schwier, Ryan Craven, R. R. Brooks, and Christopher Griffin. "Inferring Statistically Significant Hidden Markov Models," IEEE Transactions on Knowledge and Data Engineering, v.25, 2013, p. 1548.

Abramyan, T., Collier, G., Kucukkal, T.G., Li, X., Snyder, J.A., Thyparambil, A.A., Vellore, N.A., Wei, Y., Yancey, J.A., Stuart, S.J., and Latour R.A.,. "Understanding Protein-Surface Interactions at the Atomistic Level Through the Synergistic Development of Experimental and Molecular Simulation Methods," American Chemical Society, v.Protein, 2012, p. 197. 

J. A. Snyder, T. Abramyan, J. A. Yancey, A. A. Thyparambil, Y. Wei, S. J. Stuart, and R. A. Latou. "Development of a Tuned Interfacial Force Field Parameter Set for the Simulation of Protein Adsorption to Silica Glass," Biointerphases, v.7, 2012. 

3. Stephen P. Ficklin and F. Alex Feltus. "A Systems-Genetics Approach and Data Mining Tool For the Discovery of Genes Underlying Complex Traits in Oryza Sativa," PloS ONE, v.8, 2013.

F. Alex Feltus, Stephen P. Ficklin, Scott M Gibson, and Melissa C. Smith. "Maximizing Capture of Gene Co-expression Relationships Through Pre-Clustering of Input Expression Samples: An Arabidopsis Case Study," BMC Systems Biology, v.7, 2013. 

Jacob B Spangler and F. Alex Feltus.. "Conserved Noncoding Sequences are Associated with Rates of mRNA Decay in Arabidopsis.," Frontiers in Plant Science, 2013. 

6. Juan C Motamayor, Keithanne Mockaitis, Jeremy Schmutz, Niina Haiminen, Donald Livingstone, Omar Cornejo, Seth D Findley, Ping Zheng, Filippo Utro, Stefan Royaert, Christopher Saski, Jerry Jenkins, Ram Podicheti, Meixia Zhao, Brian E Scheffler, Joseph C. "The genome sequence of the most widely cultivated cacao type and its use to identify candidate genes regulating pod color," Genome Biology, v.14, 2013. 

Joshua P. Vandenbrink, Andrew H. Paterson, KC Das, Roger N. Hilten, and F. Alex Feltus. "Quantitative Models of Hydrolysis Conversion Efficiency and Biomass Crystallinity Index for Plant Breeding.," Plant Breeding, v.132, 2013, p. 252.

Scott M. Gibson, Stephen P. Ficklin, Sven Isaacson, Feng Luo, F. Alex Feltus, Melissa C. Smith. "Massive-Scale Gene Co-expression Network Construction and Robustness Testing using Random Matrix Theory," PLoS ONE, v.2, 2013.


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