Collaborative Research: SHF: Medium: Enabling GPU Performance Simulation for Large-Scale Workloads with Lightweight Simulation Methods

Information

  • NSF Award
  • 2402806
Owner
  • Award Id
    2402806
  • Award Effective Date
    5/1/2024 - a month ago
  • Award Expiration Date
    4/30/2028 - 3 years from now
  • Award Amount
    $ 377,694.00
  • Award Instrument
    Standard Grant

Collaborative Research: SHF: Medium: Enabling GPU Performance Simulation for Large-Scale Workloads with Lightweight Simulation Methods

Designing the architecture of new computer chips typically relies on detailed simulations to avoid expensive manufacturing processes. However, the speed of computer architecture simulations has not kept up with the rapid advancements in computing technology, particularly for systems that execute applications with large computations, large memory requirements, and large communication needs. This project introduces innovative lightweight simulation techniques that focus on efficiency by selectively simulating certain aspects of chip design or using higher levels of abstraction, drastically speeding up the simulation process. This project will enable the research community with techniques that support quicker development of new computing technologies. The research outcome will make the field of computer architecture more accessible to researchers with fewer resources. Moreover, the simulation techniques derived from this project will be integrated into the computer architecture curricula, helping students, especially under-resourced students, better understand concepts related to large-scale computing.<br/> <br/>Traditional computer architecture simulators recreate cycle-by-cycle details of the hardware execution, hindering fast simulation. To improve performance, this project introduces a novel suite of simulation tools designed to support the design and optimization of next-generation, large-scale computing systems. The approach encompasses three complementary strategies: behavior modeling, sampled simulations, and data-driven simulation. Behavior modeling abstracts hardware components to focus on essential performance metrics, enabling faster simulations without significant loss of accuracy. Sampled simulations leverage the repetitive nature of applications (with a special focus on GPU applications) to predict performance by simulating only critical segments of the workload. Data-driven simulations take advantage of statistical and performance modeling techniques to further advance simulation capabilities. These strategies will be unified under the Akita simulator framework, facilitating interoperability and ease of use across different simulation schemes.<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
    Danella Zhaodzhao@nsf.gov7032924434
  • Min Amd Letter Date
    4/5/2024 - a month ago
  • Max Amd Letter Date
    4/5/2024 - a month ago
  • ARRA Amount

Institutions

  • Name
    University of Rochester
  • City
    ROCHESTER
  • State
    NY
  • Country
    United States
  • Address
    910 GENESEE ST
  • Postal Code
    146113847
  • Phone Number
    5852754031

Investigators

  • First Name
    Sreepathi
  • Last Name
    Pai
  • Email Address
    sree@cs.rochester.edu
  • Start Date
    4/5/2024 12:00:00 AM

Program Element

  • Text
    Software & Hardware Foundation
  • Code
    779800

Program Reference

  • Text
    MEDIUM PROJECT
  • Code
    7924
  • Text
    COMPUTER ARCHITECTURE
  • Code
    7941