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
Title | Date |
---|---|
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 |
---|---|---|---|
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
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.