Award Abstract # 0224424
CISE Research Resources: Collaborative Research Resources: Collaborative Data Analysis and Visualization

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: REGENTS OF THE UNIVERSITY OF MINNESOTA
Initial Amendment Date: September 11, 2002
Latest Amendment Date: September 20, 2006
Award Number: 0224424
Award Instrument: Continuing Grant
Program Manager: Rita Rodriguez
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 15, 2002
End Date: December 31, 2006 (Estimated)
Total Intended Award Amount: $0.00
Total Awarded Amount to Date: $500,000.00
Funds Obligated to Date: FY 2002 = $205,400.00
FY 2003 = $203,835.00

FY 2004 = $90,765.00
History of Investigator:
  • Paul Woodward (Principal Investigator)
    paul@lcse.umn.edu
  • Ernest Retzel (Co-Principal Investigator)
  • Ted Wetherbee (Co-Principal Investigator)
  • Jon Weissman (Co-Principal Investigator)
  • David Du (Former Co-Principal Investigator)
Recipient Sponsored Research Office: University of Minnesota-Twin Cities
2221 UNIVERSITY AVE SE STE 100
MINNEAPOLIS
MN  US  55414-3074
(612)624-5599
Sponsor Congressional District: 05
Primary Place of Performance: University of Minnesota-Twin Cities
2221 UNIVERSITY AVE SE STE 100
MINNEAPOLIS
MN  US  55414-3074
Primary Place of Performance
Congressional District:
05
Unique Entity Identifier (UEI): KABJZBBJ4B54
Parent UEI:
NSF Program(s): CISE Research Resources
Primary Program Source: app-0102 
app-0103 

app-0104 
Program Reference Code(s): 9218, HPCC
Program Element Code(s): 289000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

EIA-0224424
Paul R. Woodward
David H. Du; Ernest F. Retzel; Jon B. Weissman; Ted J. Wetherbee
University of Minnesota-Twin Cities

CISE RR: Collaborative Data Analysis and Visualization

This project, creating a high-speed network that will enable coupling a large number of PCs and Macintoshes to tackle computationally-intensive problems during idle times, enhances the collaboration between the University of Minnesota Laboratory for Computational Science and Engineering (LCSE), Center for Computational Genomics and Bioinformatics (CCGB), Academic Distributed Computing Services (ADCS), the computer science research group, and Fon du Lac Tribal and Community College. The facility will be used as a platform to perform off-line batch jobs related to visualization and data mining. Augmenting network, storage, and graphics rendering capacity for a pre-existing student lab, the project will link together approximately 100 workstations at UMN via a Gigabit Ethernet network to service an interdisciplinary group or researchers. UMN collaborating teams, with on exception, will move the new Digital Technology Center (DTC) into a common location at the heart of the campus. This common location creates a special opportunity to exploit the workstations as a powerful data analysis and visualization engine for the genomics and scientific visualization applications of CCGB and the LCSE. The use of workstations during the time of low student utilization is expected to provide five-to ten-fold increases in data storage capacity and bandwidth, data mining processing power, and image rendering power. The collaboration with computer scientists, on distributed computing techniques and networked storage technology, plays a vital role in realizing the benefits. The project, involving two students from Fond du Lac Tribal College, provides platforms for research in cluster network design, cost-effective commodity-based storage area network design and operation, distributed computing, and distributed visualization. A fully connected Gigabit Ethernet network will be built by the collaborating team. This network-switching fabric will interconnect the machines of the ADCS lab and the machine and network attached storage of the CCGB and LCSE. A gigabit Ethernet link to UMN OC-12 Internet-2 connection will enable large amounts of data to be brought into the combined environment (e.g., NSF TeraGrid). The project leverages resources and expertise to create a combined capacity for data analysis and visualization far greater than existed in any one of the participating labs before the collaboration.

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