CRII: CNS: Auction Mechanism Design for Energy-Efficient High Performance Computing

Information

  • NSF Award
  • 2300124
Owner
  • Award Id
    2300124
  • Award Effective Date
    10/1/2022 - a year ago
  • Award Expiration Date
    7/31/2024 - 2 months from now
  • Award Amount
    $ 154,068.00
  • Award Instrument
    Standard Grant

CRII: CNS: Auction Mechanism Design for Energy-Efficient High Performance Computing

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.<br/><br/>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.<br/><br/>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.

  • Program Officer
    Marilyn McCluremmcclure@nsf.gov7032925197
  • Min Amd Letter Date
    11/22/2022 - a year ago
  • Max Amd Letter Date
    11/22/2022 - a year ago
  • ARRA Amount

Institutions

  • Name
    University of Toledo
  • City
    TOLEDO
  • State
    OH
  • Country
    United States
  • Address
    2801 W BANCROFT ST
  • Postal Code
    436063390
  • Phone Number
    4195302844

Investigators

  • First Name
    Kishwar
  • Last Name
    Ahmed
  • Email Address
    kishwar.ahmed@utoledo.edu
  • Start Date
    11/22/2022 12:00:00 AM

Program Element

  • Text
    CSR-Computer Systems Research
  • Code
    7354

Program Reference

  • Text
    CISE Resrch Initiatn Initiatve
  • Code
    8228
  • Text
    EXP PROG TO STIM COMP RES
  • Code
    9150