| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | August 23, 2021 |
| Latest Amendment Date: | August 23, 2021 |
| Award Number: | 2119294 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Damian Dechev
CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2021 |
| End Date: | September 30, 2023 (Estimated) |
| Total Intended Award Amount: | $150,000.00 |
| Total Awarded Amount to Date: | $150,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
10 W 35TH ST CHICAGO IL US 60616-3717 (312)567-3035 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
10 W 31st Street Chicago IL US 60616-3717 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
PPoSS-PP of Scalable Systems, PPoSS-PP of Scalable Systems |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
AI-enabled science, where advanced machine-learning technologies are used for surrogate models, autotuning, and in situ data analysis, is quickly being adopted in science and engineering for tackling complex and challenging computational problems. The wide adoption of heterogeneous systems embedded with different types of processing devices (CPUs, GPUs, and AI accelerators) further complicates the execution of AI-enabled science on supercomputers. The research for AI-enabled simulations on heterogeneous systems is far from sufficient. The project?s novelty is to explore key features essential for a scalable, energy-efficient HPC environment for AI-enabled science on heterogeneous systems. The unified team of researchers tackles the problem in a cross-layer manner, focusing on the synergies among application algorithms, programming languages and compilers, runtime systems, and high-performance computing. The project's impact is to catalyze scientific discoveries by making scientific computing faster, more scalable and more energy-efficient.
The long-term research vision is to develop SEEr, a scalable, energy-efficient HPC environment for scaling up and accelerating AI-enabled science for scientific discovery. This planning project explores fundamental questions to realize the research vision. The team focuses on scalable surrogate models for an incompressible computational fluid dynamics application using OpenFOAM, cost models for this application on heterogeneous resources, dynamic task mapping for efficient execution, and performance and power monitoring and characterization to explore tradeoffs among performance, scalability, and energy efficiency on a state-of-the-art testbed named Polaris.
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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
AI-enabled science, where advanced machine-learning technologies are used for surrogate models, auto tuning, and in situ data analysis, is quickly being adopted in science and engineering for tackling complex and challenging computational problems. The wide adoption of heterogeneous systems embedded with different types of processing devices (CPUs, GPUs, and AI accelerators) further complicates the execution of AI-enabled science on supercomputers. The research for AI-enabled simulations on heterogeneous systems is far from sufficient.
This planning project is to conduct initial studies leading to a full proposal to develop SEEr, a Scalable, Energy-Efficient HPC environment for scaling up and accelerating AI-enabled science for scientific discovery. The project has two key outcomes. One is the performance and power characterization of two AI-enabled computational fluid dynamics (CFD) benchmarks on two production systems. The other is a deeper understanding of key roadblocks of executing AI-enabled applications on heterogeneous CPU-GPU systems. Together, these outcomes enable the team to identify the components essential for building a science environment suitable for supporting AI-enabled science on heterogeneous systems. Another outcome is the thought-provoking meetings with domain scientists throughout the project period.
The project advances the scientific understanding of AI-enabled applications on heteronomous CPU-GPU systems. It also explores key features essential for providing a scalable, energy-efficient computing environment for AI-enabled science on heterogeneous systems.
This project results in six technical reports and several referred technical publications, an open-source profiling tool, and real experimental data (runtime, power, hardware counters, etc) on the production systems. Another key impact is the training of five students, ranging from undergraduate students to doctoral students, in the field of high-performance computing. Among the five participating students, two are from underrepresented groups.
Last Modified: 10/26/2023
Modified by: Zhiling Lan
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