Name | Region | Skills | Interests |
---|---|---|---|
Aaron Jezghani | Campus Champions | ||
Christopher Bl… | Campus Champions | ||
Deborah Penchoff | Campus Champions | ||
Daniel Howard | ACCESS CSSN, Campus Champions, CCMNet, RMACC | ||
Edwin Posada | Campus Champions | ||
Jason Wells | ACCESS CSSN, Campus Champions | ||
Jason Yalim | Campus Champions | ||
Katia Bulekova | ACCESS CSSN, Campus Champions, CAREERS, CCMNet, Northeast | ||
Nicholas Danes | Campus Champions, MINES | ||
Justin Oelgoetz | Campus Champions, CCMNet | ||
Paul Rulis | Campus Champions | ||
Rebecca Belshe | Campus Champions, CCMNet | ||
Ron Rahaman | Campus Champions | ||
Sean Anderson | Campus Champions, CCMNet | ||
Xiaoqin Huang | ACCESS CSSN | ||
Xiaoge Wang | Campus Champions |
Title | Date |
---|---|
2025 UCAR Improving Scientific Software Conference | 4/07/25 |
Title | Category | Tags | Skill Level |
---|---|---|---|
Cornell Virtual Workshop | Learning | jetstream, stampede2, cloud-computing, data-analysis, performance-tuning, parallelization, file-transfer, globus, slurm, training, cuda, matlab, python, r, mpi | Beginner, Intermediate, Advanced |
Fine-tuning LLMs with PEFT and LoRA | Video | faster, optimization, performance-tuning, tuning | Intermediate, Advanced |
GPU Computing Workshop Series for the Earth Science Community | Learning | optimization, performance-tuning, profiling, parallelization, github, pytorch, tensorflow, oceanography, gpu, hpc-arch-and-perf, training, c, c++, fortran, cuda, jupyterhub, programming, programming-best-practices, python | Beginner |
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 Corporation for Atmospheric Research
ACCESS CSSN, Campus Champions, CCMNet, RMACC
mentor, research computing facilitator, research software engineer, CCMNet
Arizona State University
Campus Champions, CCMNet
research computing facilitator, CCMNet
Cornell University
ACCESS CSSN
mentor, researcher/educator, research computing facilitator, research software engineer, ci systems engineer, Consultant
CCMNet
CCMNet