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Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF OREGON
Doing Business As Name:University of Oregon Eugene
PD/PI:
  • Jee W Choi
  • (404) 729-0795
  • jeec@uoregon.edu
Co-PD(s)/co-PI(s):
  • Boyana Norris
Award Date:08/30/2021
Estimated Total Award Amount: $ 75,000
Funds Obligated to Date: $ 75,000
  • FY 2021=$75,000
Start Date:10/01/2021
End Date:09/30/2022
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: PPoSS: Planning: Extreme-scale Sparse Data Analytics
Federal Award ID Number:2119047
DUNS ID:079289626
Parent DUNS ID:049793995
Program:PPoSS-PP of Scalable Systems
Program Officer:
  • Damian Dechev
  • (703) 292-8910
  • ddechev@nsf.gov

Awardee Location

Street:5219 UNIVERSITY OF OREGON
City:Eugene
State:OR
ZIP:97403-5219
County:Eugene
Country:US
Awardee Cong. District:04

Primary Place of Performance

Organization Name:University of Oregon Eugene
Street:
City:
State:OR
ZIP:97403-5219
County:Eugene
Country:US
Cong. District:04

Abstract at Time of Award

The graph data structure is used for storing and manipulating relational data. Tensors are a higher-order generalization of the two-dimensional matrix representation. Both graphs and tensors are used in exploratory and automated data analysis. Applications areas include cybersecurity, complex system analysis, and personalized healthcare. There exist a myriad of known algorithms for typical data analysis tasks in these areas. For instance, the problem of community identification in graphs, referring to automatically identifying well-connected groups of vertices in graphs, has dozens of algorithms. Analogous to the singular value decomposition in matrices, several tensor factorizations exist with diverse use-cases. Both graph algorithms and tensor factorizations use computer storage formats inspired by matrix computations. This project focuses on data analysis use-cases that result in large-scale graphs and tensors, necessitating parallel and distributed processing. The project's novelties are in identifying and developing unifying parallel algorithm design principles that span multiple graph computations and tensor factorizations. In the planning stage, several focused research tasks will explore eight unifying themes. The project aims to develop the foundations for an end-to-end streaming data analytics system with performance comparable to highly tuned static graph analysis benchmarks on current high-end workstations and supercomputers. The investigators' multi-disciplinary expertise span high-performance computing, theory and algorithms, computer architecture, and programming languages and compilers. The cross-cutting research aims include generalizable principles to orchestrate intra- and inter-node communication, multiple approaches for exploiting hierarchical parallelism, locality-enhancing strategies, and automatic performance tuning. The software artifacts from the planning stage could form the basis for new data analytic benchmarks. The investigators will incorporate research findings into the courses they teach. Engaging experts from the national laboratories and the industry in the planning stage will help solidify future large-scale efforts. The investigators will leverage and contribute to existing institutional programs that broaden participation in computing research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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