| NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
| Recipient: |
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| Initial Amendment Date: | January 10, 2023 |
| Latest Amendment Date: | January 10, 2023 |
| Award Number: | 2312673 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Juan Li
jjli@nsf.gov (703)292-2625 OAC Office of Advanced Cyberinfrastructure (OAC) CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | January 1, 2023 |
| End Date: | December 31, 2027 (Estimated) |
| Total Intended Award Amount: | $467,770.00 |
| Total Awarded Amount to Date: | $467,770.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
107 S INDIANA AVE BLOOMINGTON IN US 47405-7000 (317)278-3473 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
107 S INDIANA AVE BLOOMINGTON IN US 47405-7000 |
| 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): | CAREER: FACULTY EARLY CAR DEV |
| 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
This CAREER project researches and develops novel algorithms and software to improve the efficacy, usability, performance, and scalability of data reduction for high-performance computing (HPC) systems and applications. It contributes to the cyberinfrastructure (CI) of big data management for HPC applications in many domains such as cosmology, climatology, seismology, and machine learning. The research findings will be widely disseminated through open-source software packages and publications in premier conferences and journals. An integrated educational and outreach program is designed to foster CI workforce development, including integration of concepts and use of data reduction in curricula, research training for undergraduate and graduate students, and a specially designed training program for scientists and engineers from universities and national labs.
This CAREER project simultaneously addresses these four critical issues in scientific data reduction through comprehensive analytical modeling and architectural performance optimization. Specific scientific contributions include: (1) it builds lightweight models to accurately estimate the compression ratio and quality of different techniques in the prediction and encoding stages of prediction-based compression, and optimizes the compression configurations to maximize the compression ratio under compression quality constraints; (2) it develops new efficient predictors and lossless encoding methods for lossy compression of scientific data on GPUs with deep architectural optimizations to achieve both high throughput and ratio; and (3) it deeply integrates the optimized compression with parallel I/O and MPI libraries with a series of optimizations to improve the performance of data movements and the scalability of HPC applications. The success of this research agenda enables scientists and engineers to well address the increasingly severe challenge of scientific data explosion.
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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