cuda
Affinity Groups
Logo | Name | Description | Tags | Join |
---|---|---|---|---|
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
Upcoming Events & Trainings
Title | Date |
---|---|
A Hands-On Introduction to Quantum Computing with NVIDIA’s CUDA Quantum | 4/19/24 |
HPC and Data Science Summer Institute | 8/05/24 |
Topics from Ask.CI
CI Links
Title | Category | Tags | Skill Level |
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Examples of Thrust code for GPU Parallelization | Learning | parallelization, gpu, cuda | Intermediate, Advanced |
Cornell Virtual Workshop | Learning | performance-tuning, parallelization, file-transfer, globus, slurm, cuda, matlab, python, r, mpi | Beginner, 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 |
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
Cody Stevens
Wake Forest University
Programs
Campus Champions
Roles
regional facilitator, researcher/educator, research computing facilitator, ci systems engineer
Expertise
Andrew Sherman
Yale University
Programs
ACCESS CSSN, Campus Champions, CAREERS
Roles
mentor, research computing facilitator, steering committee, regional admin, Match SC
Expertise
People with Interest
Paul Rulis
University of Missouri-Kansas City
Programs
Campus Champions
Roles
researcher/educator, research computing facilitator
Interests
Katia Bulekova
Boston University
Programs
ACCESS CSSN, Campus Champions, CAREERS, Northeast
Roles
mentor, research computing facilitator
Interests
William Lai
Cornell University
Programs
ACCESS CSSN
Roles
mentor, researcher/educator, research computing facilitator, research software engineer, ci systems engineer, Consultant