Project Grant 2452082

Award Date 10/1/25
Completion Date 9/30/28
Dollars Obligated $270K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Atlanta, GA 30332, USA
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This Project Grant award of $270,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to develop scalable simulation techniques and advanced memory management strategies for large-scale machine learning workloads on graphics processing units (GPUs).

The key objectives are to enable faster and more efficient simulation of large machine learning models, which can take days or weeks using current methods, and to improve GPU memory utilization when model parameters exceed on-device memory capacity. The research aims to leverage the unique characteristics of machine learning computations, such as optimized library functions and value distributions, to develop innovative approaches for accelerating simulation and compressing GPU memory usage. The outcomes of this research are intended to facilitate broader adoption of GPUs across diverse computing domains and drive further innovation in computational science. No sub-awards are planned for this award, which has an ultimate completion date of September 30, 2028.

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