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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF SOUTH CAROLINA
Doing Business As Name:University of South Carolina at Columbia
PD/PI:
  • Kishwar Ahmed
  • (843) 208-8314
  • ahmedk@uscb.edu
Award Date:05/11/2021
Estimated Total Award Amount: $ 174,770
Funds Obligated to Date: $ 174,770
  • FY 2021=$174,770
Start Date:08/01/2021
End Date:07/31/2023
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:CRII: CNS: Auction Mechanism Design for Energy-Efficient High Performance Computing
Federal Award ID Number:2104925
DUNS ID:041387846
Parent DUNS ID:041387846
Program:CSR-Computer Systems Research
Program Officer:
  • Marilyn McClure
  • (703) 292-5197
  • mmcclure@nsf.gov

Awardee Location

Street:Sponsored Awards Management
City:COLUMBIA
State:SC
ZIP:29208-0001
County:Columbia
Country:US
Awardee Cong. District:06

Primary Place of Performance

Organization Name:University of South Carolina Beaufort
Street:1 University Blvd
City:Bluffton
State:SC
ZIP:29909-6085
County:Okatie
Country:US
Cong. District:01

Abstract at Time of Award

High performance computing (HPC) systems (such as supercomputers) are generally large infrastructures containing thousands of server nodes that can perform computations in a fast and efficient manner. HPC systems can consume an enormous amount of power during their operation. For example, current top-ranked supercomputers can consume tens of megawatts of power during peak operation. As a direct consequence of power consumption increase, energy cost has become a major component of the overall cost of the operation of an HPC system. To achieve energy sustainability in HPC, this project plans to develop novel models to reduce energy cost and contribute to the power system stability. There are three primary objectives of this project: (1) develop machine learning models to predict the power and performance of parallel applications; (2) develop an auction mechanism model to reduce HPC system’s energy cost via collective energy reduction of HPC users, while incorporating the renewable energy generation into the model; and (3) experiment and validate the proposed auction mechanism model via simulation. Overall, the project is expected to reduce the energy cost of large-scale systems, as well as to achieve power grid energy conservation and stability. This project will contribute towards advancement of the state-of-the-art in energy-efficiency of HPC, as well as to balance the energy-performance trade-offs in HPC. In doing so, this project will increase HPC system’s participation in sustainable computing. The proposed research will enable HPC systems to closely interact with the power grid system, and enable feedback-based energy reduction based on electricity price variation and renewable energy generation. This project will increase research participation of both graduate and undergraduate students. Additionally, the project will train and educate students in the area of parallel and high performance computing, and energy-efficient computing. Furthermore, through various outreach activities and research involvement, the project plans to promote diversity in computing by involving underrepresented groups. 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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