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Mentors and Regional Facilitators
Name Region Skills Interests
Adam Carlson Campus Champions
Aaron Jezghani Campus Champions
Anita Schwartz Campus Champions
Andrew Sherman ACCESS CSSN, Campus Champions, CAREERS
Brian Gregor ACCESS CSSN, Northeast, Campus Champions
Kevin Bryan Campus Champions
Craig Gross Campus Champions, CCMNet
Cody Stevens Campus Champions, CCMNet
Cesar Sul ACCESS CSSN, Campus Champions
Bala Desinghu ACCESS CSSN, Campus Champions, CAREERS, Northeast
Deborah Penchoff Campus Champions
Daniel Howard ACCESS CSSN, Campus Champions, CCMNet, RMACC
Darshan Sarojini ACCESS CSSN
Elie Alhajjar ACCESS CSSN, CCMNet
Eden Furtak-Cole Campus Champions
Eric Brown Great Plains, Northeast
Fernando Garzon ACCESS CSSN
Gaurav Khanna Campus Champions, CAREERS, Northeast, CCMNet
Gil Speyer ACCESS CSSN, RMACC, Campus Champions
Iman Rahbari Campus Champions, ACCESS CSSN
Yu-Chieh Chi Campus Champions
Jonathan Komperda Campus Champions
Jason Wells ACCESS CSSN, Campus Champions
Jason Yalim Campus Champions
Kenneth Bundy CAREERS
Lakitha Wijeratne TRECIS
Mohsen Ahmadkhani CCMNet, ACCESS CSSN
Martin Cuma RMACC, Campus Champions
Mattie Niznik Campus Champions
Michael Puerrer Campus Champions, Northeast
Jeffrey J. Nuc… CAREERS, CCMNet
Justin Oelgoetz Campus Champions, CCMNet
Rebecca Belshe Campus Champions, CCMNet
Mike Renfro Campus Champions, CCMNet
Shaohao Chen Northeast
Swabir Silayi ACCESS CSSN, CCMNet, Campus Champions
Sebastian Sensale Campus Champions
Sathish Srinivasan ACCESS CSSN
Tyler Burkett Kentucky
Xiaoge Wang Campus Champions

Affinity Groups

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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
Bayesian nonparametric ensemble air quality model predictions at high spatio-temporal daily nationwide  1 km grid cell
Columbia University

I aim to run a Bayesian Nonparametric Ensemble (BNE) machine learning model implemented in MATLAB. Previously, I successfully tested the model on Columbia's HPC GPU cluster using SLURM. I have since enabled MATLAB parallel computing and enhanced my script with additional lines of code for optimized execution. 

I want to leverage ACCESS Accelerate allocations to run this model at scale.

The BNE framework is an innovative ensemble modeling approach designed for high-resolution air pollution exposure prediction and spatiotemporal uncertainty characterization. This work requires significant computational resources due to the complexity and scale of the task. Specifically, the model predicts daily air pollutant concentrations (PM2.5​ and NO2 at a 1 km grid resolution across the United States, spanning the years 2010–2018. Each daily prediction dataset is approximately 6 GB in size, resulting in substantial storage and processing demands.

To ensure efficient training, validation, and execution of the ensemble models at a national scale, I need access to GPU clusters with the following resources:

  • Permanent storage: ≥100 TB
  • Temporary storage: ≥50 TB
  • RAM: ≥725 GB

In addition to MATLAB, I also require Python and R installed on the system. I use Python notebooks to analyze output data and run R packages through a conda environment in Jupyter Notebook. These tools are essential for post-processing and visualization of model predictions, as well as for running complementary statistical analyses.

To finalize the GPU system configuration based on my requirements and initial runs, I would appreciate guidance from an expert. Since I already have approval for the ACCESS Accelerate allocation, this support will help ensure a smooth setup and efficient utilization of the allocated resources.

Status: Complete

People with Expertise

Carlos Paniagua

Center for Computation and Visualization - Brown University

Programs

CAREERS

Roles

researcher/educator

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Expertise

Anita Schwartz

University of Delaware

Programs

Campus Champions

Roles

mentor, research computing facilitator, Affinity Group Leader, steering committee

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Expertise

Jonathan Blanchard

Western New England University

Programs

Northeast

Roles

student-facilitator, mentee

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Expertise

People with Interest

Ana Marija Sokovic

University of Illinois at Chicago

Programs

CCMNet

Roles

CCMNet

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Interests

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Matthew Chung

University of California Riverside

Programs

Campus Champions

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Interests

Gil Speyer

Arizona State University

Programs

ACCESS CSSN, RMACC, Campus Champions

Roles

mentor, researcher/educator, research computing facilitator, Affinity Group Leader, CIP

Interests