Award Abstract # 2229304
POSE: Phase I: Toward a Task-Parallel Programming Ecosystem for Modern Scientific Computing

NSF Org: TI
Translational Impacts
Recipient: UNIVERSITY OF UTAH
Initial Amendment Date: September 9, 2022
Latest Amendment Date: September 9, 2022
Award Number: 2229304
Award Instrument: Standard Grant
Program Manager: Deepankar Medhi
dmedhi@nsf.gov
 (703)292-2935
TI
 Translational Impacts
TIP
 Directorate for Technology, Innovation, and Partnerships
Start Date: September 15, 2022
End Date: October 31, 2023 (Estimated)
Total Intended Award Amount: $298,814.00
Total Awarded Amount to Date: $298,814.00
Funds Obligated to Date: FY 2022 = $259,070.00
History of Investigator:
  • Tsung-Wei Huang (Principal Investigator)
    tsung-wei.huang@wisc.edu
Recipient Sponsored Research Office: University of Utah
201 PRESIDENTS CIR
SALT LAKE CITY
UT  US  84112-9049
(801)581-6903
Sponsor Congressional District: 01
Primary Place of Performance: University of Utah
201 PRESIDENTS CIR
SALT LAKE CITY
UT  US  84112-9049
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): LL8GLEVH6MG3
Parent UEI:
NSF Program(s): POSE
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 211Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.084

ABSTRACT

Open-source software systems designed for task-parallel programming have become central to a wide range of modern scientific computing applications, such as machine learning and quantum computing. While decades of research has yielded many open-source task-parallel programming systems, most of them are led by a handful of developers and their impacts do not sustain in the long run. To overcome this challenge, this project proposes scoping activities to establish a route to a long-term sustainable ecosystem for task-parallel programming. These activities build atop the open-source software, Taskflow, a high-performance task-parallel system to streamline the building of complex scientific computing applications.

This project starts by discovering an ecosystem based on three increasing applications of Taskflow, quantum computing, circuit design automation, and multimedia. The discovery effort consists of designing showcase software products and organizing workshops to pursue the optimal ecosystem for Taskflow. Then, the project designs a series of developer training programs to engage potential content contributors who can help develop and maintain Taskflow in a community-driven fashion. Finally, the project establishes a transparent and publicly visible governance model to formalize the decision-making process of Taskflow for various elements, including technical contributions, partnership, and security policies.

This project lays the foundation for Taskflow to transition to a robust and sustainable system asset for the scientific computing community to quickly respond to emerging parallelism using scalable task-based approaches. Many scoping activities in this project can instill confidence in commercial adoption of Taskflow and grow its partnership with other open-source scientific computing projects to further broaden its impact and enhance sustainability. Also, results produced in this project can be used in classrooms to renovate existing learning materials for high-performance computing and software practice, engaging a diverse group of students in open-source development.

The project website is available at https://taskflow.github.io/ which contains the latest news, source code, step-by-step learning materials, and benchmarks. The authors are committed to developing and maintaining the project in the long term.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Dian-Lun Lin, Yanqing Zhang "GenFuzz: GPU-accelerated Hardware Fuzzing using Genetic Algorithm with Multiple Inputs" ACM/IEEE Design Automation Conference (DAC) , 2023 Citation Details
Guo, Guannan and Huang, Tsung-Wei and Wong, Martin "Fast STA Graph Partitioning Framework for Multi-GPU Acceleration" IEEE/ACM Design, Automation and Test in Europe Conference (DATE) , 2023 https://doi.org/10.23919/DATE56975.2023.10137050 Citation Details
Huang, Tsung-Wei "qTask: Task-parallel Quantum Circuit Simulation with Incrementality" IEEE International Parallel and Distributed Processing Symposium (IPDPS) , 2023 https://doi.org/10.1109/IPDPS54959.2023.00080 Citation Details

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