nvidia
Affinity Groups
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 |
Announcements
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 |
Upcoming Events & Trainings
Topics from Ask.CI
Knowledge Base Resources
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 |
Engagements
GPU-accelerated Ice Sheet Flow Modeling
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.
People with Expertise
Christopher Blanton, Ph.D.
North Carolina State University at Raleigh
Programs
Campus Champions
Roles
CampusChampionsAdmin, research computing facilitator, research software engineer
Expertise
Expertise
Ron Rahaman
Georgia Institute of Technology
Programs
Campus Champions
Roles
research software engineer
Expertise
People with Interest
Xiaoqin Huang
Rice University
Programs
ACCESS CSSN
Roles
mentor, research computing facilitator, research software engineer, cssn
Interests
Daniel Howard
University Corporation for Atmospheric Research
Programs
ACCESS CSSN, Campus Champions, CCMNet, RMACC
Roles
mentor, research computing facilitator, research software engineer, CCMNet