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Title Category Tags Skill Level
ACCESS HPC Workshop Series Learning deep-learningmachine-learningneural-networks +12 more tags Beginner, Intermediate
AI for improved HPC research - Cursor and Termius - Powerpoint Slides documentationaimachine-learning +5 more tags Beginner, Intermediate
AI powered VsCode Editor Tool aimachine-learningworkflow +4 more tags Beginner, Intermediate

Engagements

GPU-accelerated Ice Sheet Flow Modeling
University of North Dakota

Sea levels are rising (3.7 mm/year and increasing!)! The primary contributor to rising sea levels is enhanced polar ice discharge due to climate change. However, their dynamic response to climate change remains a fundamental uncertainty in future projections. Computational cost limits the simulation time on which models can run to narrow the uncertainty in future sea level rise predictions. The project's overarching goal is to leverage GPU hardware capabilities to significantly alleviate the computational cost and narrow the uncertainty in future sea level rise predictions. Solving time-independent stress balance equations to predict ice velocity or flow is the most computationally expensive part of ice-sheet simulations in terms of computer memory and execution time. The PI developed a preliminary ice-sheet flow GPU implementation for real-world glaciers. This project aims to investigate the GPU implementation further, identify bottlenecks and implement changes to justify it in the price to performance metrics to a "standard" CPU implementation. In addition, develop a performance portable hardware (or architecture) agnostic implementation.

Status: Complete

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Xilin Qiao

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Yun Shen

Boston University

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Micholas Smith

University of Tennessee, Knoxville

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James Chen

New York University

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Fernando Garzon

University of California, San Diego

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mentor, research software engineer

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Chimdia Kabuo

Indiana University-Purdue University Fort Wayne

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