Affinity Groups

Logo Name Description Tags Join
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… fastertamucomposable-systems +1 more tags Login to join
Anvil Purdue University is the home of Anvil, a powerful new supercomputer that provides advanced computing capabilities to support a wide range of computational and data-intensive research spanning from… aimachine-learninganvil +1 more tags 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… affinity-groupcomputer-graphicsvisualization +3 more tags Login to join

Announcements

Upcoming Events & Trainings

No events or trainings are currently scheduled.

Topics from Ask.CI

Loading topics from Ask.CI ...

Knowledge Base Resources

Title Category Tags Skill Level
ACCESS HPC Workshop Series Learning deep-learningmachine-learningneural-networks +12 more tags Beginner, Intermediate
ACCESS KB Guide - Expanse Docs expansecomposable-systemsgpu Beginner, Intermediate, Advanced
ACES: Charliecloud Containers for Scientific Workflows (Tutorial) Learning ACESTAMUSCRATCH +10 more tags Beginner

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

People with Expertise

Devin Bayly

University of Arizona

Programs

ACCESS CSSN, Campus Champions, CCMNet

Roles

research computing facilitator, Affinity Group Leader, CCMNet

User

Expertise

Grant Scott

University of Missouri

Programs

Great Plains

Roles

mentor, steering committee

Expertise

+31 more tags

Brian Haymore

University of Utah

Programs

Campus Champions, RMACC

Roles

mentor, research computing facilitator, ci systems engineer

Brian Haymore

Expertise

+41 more tags

People with Interest

Matthew Chung

University of California, Riverside

Programs

ACCESS CSSN, CCMNet

Roles

student-facilitator, cssn, CCMNet

Placeholder headshot

Interests

Ana Marija Sokovic

University of Illinois at Chicago

Programs

CCMNet

Roles

CCMNet

Placeholder headshot

Interests

+69 more tags

Matt Ferguson

Boise State University

Programs

At-Large

Roles

researcher/educator, cssn

picture of Matt Ferguson

Interests