gpu
Affinity Groups
Logo | Name | Description | Tags | Join |
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FASTER | Fostering Accelerated Scientific Transformations, Education, and Research (FASTER) is a NSF-MRI-funded cluster (award number 2019129) that offers state of the art CPUs, GPUs, and NVMe (Non-Volatile… | Login to join | ||
High Performance Visualization | This group first and foremost is a space for those experimenting and learning to leverage High Performance Compute environments for visualization. To be a part of this community you don't need to be… | Login to join | ||
Expanse | Expanse is a dedicated ACCESS cluster designed by Dell and SDSC delivering 5.16 peak petaflops, and will offer Composable Systems and Cloud Bursting. Expanse's standard compute nodes are each… | Login to join |
Announcements
Title | Date |
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NVIDIA GenAI/LLM Virtual Workshop Series for Higher Ed | 02/17/24 |
Upcoming Events & Trainings
Title | Date |
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ACES: AlphaFold Protein Structure Prediction | 4/08/25 |
ACES: GPU Programming (CUDA) | 4/22/25 |
Topics from Ask.CI
Knowledge Base Resources
Title | Category | Tags | Skill Level |
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ACCESS HPC Workshop Series | Learning | deep-learning, machine-learning, neural-networks, big-data, tensorflow, gpu, training, openmpi, c, c++, fortran, openmp, programming, mpi, spark | Beginner, Intermediate |
ACCESS KB Guide - Expanse | Docs | expanse, composable-systems, gpu | Beginner, Intermediate, Advanced |
ACES: Charliecloud Containers for Scientific Workflows (Tutorial) | Learning | ACES, TAMU, scratch, lammps, tensorflow, open-ondemand, gpu, nfs, slurm, bash, training, python, containers | Beginner |
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
Expertise
Brian Haymore
University of Utah
Programs
Campus Champions, RMACC, CCMNet
Roles
mentor, research computing facilitator, ci systems engineer, CCMNet

Expertise

Expertise
People with Interest
Interests
Dung Vu
California State University-San Bernardino
Programs
ACCESS CSSN, Campus Champions
Roles
research computing facilitator, CIP
Interests
Devin Bayly
University of Arizona
Programs
ACCESS CSSN, Campus Champions, CCMNet
Roles
research computing facilitator, Affinity Group Leader, CCMNet
