CAREER: Efficient and Scalable Large Foundational Model Training on Supercomputers for Sci — NSF Award to Rutgers University New B
Deep learning (DL) methods, especially the large foundational models, enable exciting new approaches to problems in many science and engineering disciplines, such as genomics, bioinformatics, meteorology, and natural language processing. Training foundational models at extreme scales is time-consuming, prone to low uti
| Award title | CAREER: Efficient and Scalable Large Foundational Model Training on Supercomputers for Sci |
|---|---|
| Award ID | 2340011 |
| Awardee | Rutgers University New Brunswick |
| City | NEW BRUNSWICK |
| State | NJ |
| Amount obligated | $599,707 |
| Principal investigator | Zhao Zhang |
| Program | CAREER: FACULTY EARLY CAR DEV |
| Start date | 07/01/2024 |
| Abstract | Deep learning (DL) methods, especially the large foundational models, enable exciting new approaches to problems in many science and engineering disciplines, such as genomics, bioinformatics, meteorology, and natural language processing. Training foundational models at extreme scales is time-consuming, prone to low utilization with limited scalability, and human-effort demanding. This NSF CAREER project addresses the convergence, performance, and scalability gaps of large foundational model pre- |
| Source | NSF Awards |
$799/mo
Try NSFGrants →