This Project Grant award, funded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research to reform profiling techniques for improving the performance of GPU-accelerated deep learning workloads. The $221,138 award, effective from July 1, 2025 to June 30, 2030, aims to advance state-of-the-art profiling methods and enable systemic performance tuning across the multiple abstraction layers of deep learning models. The research will develop innovative analysis techniques, including unified binary code analysis, incremental analysis, and data object analysis, to identify and address inefficiencies in deep learning model implementation. The project's broader significance lies in deepening the understanding of performance issues in deep learning, advancing code analysis capabilities, and integrating the findings into computer science education to cultivate a skilled workforce in performance optimization. No sub-awards are planned for this grant.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $221.1k | 1/14/25 |