Name | Region | Skills | Interests |
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Ami Gaspar | Campus Champions, Northeast | ||
Andrew Monaghan | ACCESS CSSN | ||
Alyssa Pivirotto | ACCESS CSSN, Campus Champions | ||
Kevin Bryan | Campus Champions | ||
Carrie Brown | CAREERS, ACCESS CSSN, CCMNet | ||
Craig Gross | Campus Champions, CCMNet | ||
Beau Christ | Campus Champions | ||
Cody Stevens | Campus Champions, CCMNet | ||
David Oury | Northeast | ||
Bala Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
David Carlson | ACCESS CSSN, OnDemand | ||
Daniel Morales | Campus Champions | ||
Deborah Penchoff | Campus Champions | ||
Derek Strong | Campus Champions | ||
Elie Alhajjar | ACCESS CSSN, CCMNet | ||
Elizabeth Kwon | Campus Champions, CCMNet | ||
Feseha Abebe-Akele | CCMNet | ||
Gil Speyer | ACCESS CSSN, RMACC, Campus Champions | ||
James Deaton | ACCESS CSSN, Campus Champions, Great Plains | ||
Juan Jose Garc… | ACCESS CSSN | ||
Juanjo Garcia Mesa | Campus Champions, CCMNet, ACCESS CSSN | ||
Jordan Hayes | Campus Champions | ||
Jacob Pessin | Northeast | ||
Jason Wells | ACCESS CSSN, Campus Champions | ||
Katia Bulekova | ACCESS CSSN, Campus Champions, CAREERS, CCMNet, Northeast | ||
Kenneth Bundy | CAREERS | ||
shuai liu | ACCESS CSSN | ||
Mohsen Ahmadkhani | CCMNet, ACCESS CSSN | ||
Martin Cuma | RMACC, Campus Champions | ||
Mattie Niznik | Campus Champions | ||
Andrew Monaghan | RMACC, Campus Champions | ||
Michael Puerrer | Campus Champions, Northeast | ||
Nick Dusek | Campus Champions | ||
Nicholas Panchy | Campus Champions | ||
Rebecca Belshe | Campus Champions, CCMNet | ||
Rob Harbert | Northeast | ||
Mara Sedlins | RMACC | ||
Swabir Silayi | ACCESS CSSN, CCMNet, Campus Champions | ||
Sathish Srinivasan | ACCESS CSSN | ||
Thomas Pranzatelli | |||
Xiaoge Wang | Campus Champions | ||
Wesley Brashear | ACCESS CSSN, Campus Champions | ||
William Lai | ACCESS CSSN | ||
Yun Shen | CAREERS, Northeast, ACCESS CSSN, CCMNet |
Logo | Name | Description | Tags | Join |
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R for HPC | People who use r on HPC systems and want to exchange experiences, best practices and/or collaborate. | Login to join |
Title | Date |
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COMPLECS: Code Migration | 6/12/25 |
COMPLECS: Code Migration | 6/12/25 |
COMPLECS: Code Migration | 6/12/25 |
Title | Category | Tags | Skill Level |
---|---|---|---|
Automated Machine Learning Book | Learning | ai, data-analysis, deep-learning, machine-learning, neural-networks, python, r | Intermediate, Advanced |
CI Computing Module For All | Learning | ai, computer-vision, neural-networks, visualization, big-data, gis, parallelization, data-management, data-science, bioinformatics, open-ondemand, hpc-getting-started, cpu-architecture, distributed-computing, job-submission, jupyterhub, python, r, cybersecurity, containers | Beginner |
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 |
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:
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.
Florida International University
Campus Champions
research computing facilitator, student champion