Name | Region | Skills | Interests |
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Devin Bayly | ACCESS CSSN, Campus Champions, CCMNet | ||
Christopher Bl… | Campus Champions | ||
Cody Stevens | Campus Champions, CCMNet | ||
Chris Reidy | Campus Champions, CCMNet | ||
Deborah Penchoff | Campus Champions | ||
Daniel Howard | ACCESS CSSN, Campus Champions, CCMNet, RMACC | ||
Jeff Falgout | Campus Champions, RMACC | ||
Jordan Hayes | Campus Champions | ||
Jeffrey Weekley | Campus Champions | ||
Katia Bulekova | ACCESS CSSN, Campus Champions, CAREERS, CCMNet, Northeast | ||
Rebecca Belshe | Campus Champions, CCMNet | ||
Ron Rahaman | Campus Champions | ||
Ruben Lara | Campus Champions | ||
Xiaoqin Huang | ACCESS CSSN | ||
Trey Breckenridge | Campus Champions |
Logo | Name | Description | Tags | Join |
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Jetstream-2 | Jetstream2 is a transformative update to the NSF’s science and engineering cloud infrastructure and provides 8 petaFLOPS of supercomputing power to simplify data analysis, boost discovery, and… | Login to join |
Title | Date |
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Ookami features two new NVIDIA Grace CPU Superchips | 04/17/24 |
NVIDIA GenAI/LLM Virtual Workshop Series for Higher Ed | 02/17/24 |
Title | Category | Tags | Skill Level |
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AI/ML TechLab - Accelerating AI/ML Workflows on a Composable Cyberinfrastructure | Docs | ACES, documentation, TAMU, ai, visualization, deep-learning, machine-learning, neural-networks, login, authentication, composable-systems, gpu, nvidia, slurm, bash, modules, vim, anaconda, conda, programming, python, scikit-learn | Intermediate |
Introduction to Parallel Programming for GPUs with CUDA | Learning | gpu, nvidia, c, c++, cuda | Intermediate |
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.
University of California - Riverside
Campus Champions
research computing facilitator
Arizona State University
Campus Champions, CCMNet
research computing facilitator, CCMNet