Name | Region | Skills | Interests |
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Tony Elam | Kentucky | ||
Alana Romanella | Campus Champions | ||
Brian Gregor | ACCESS CSSN, Northeast, Campus Champions | ||
Bala Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
Deborah Penchoff | Campus Champions | ||
Dylan Perkins | ACCESS CSSN, RMACC | ||
David Ryglicki | |||
Fernando Garzon | ACCESS CSSN | ||
Feseha Abebe-Akele | CCMNet | ||
Feng George Yu | Campus Champions | ||
Jacob Fosso Tande | ACCESS CSSN, Campus Champions, CCMNet | ||
Jordan Hayes | Campus Champions | ||
Jacob Pessin | Northeast | ||
Katia Bulekova | ACCESS CSSN, Campus Champions, CAREERS, CCMNet, Northeast | ||
Thomas Langford | Campus Champions, CAREERS | ||
shuai liu | ACCESS CSSN | ||
Mohsen Ahmadkhani | CCMNet, ACCESS CSSN | ||
Mahmoud Parvizi | Campus Champions | ||
Maryam Taeb | |||
Neil McGlohon | CAREERS | ||
Jeffrey J. Nuc… | CAREERS, CCMNet | ||
Rebecca Belshe | Campus Champions, CCMNet | ||
Rob Harbert | Northeast | ||
Grant Scott | Great Plains | ||
Simon Delattre | |||
Suhong Li | CAREERS, ACCESS CSSN | ||
Sathish Srinivasan | ACCESS CSSN | ||
Scott Valcourt | Northeast, Campus Champions | ||
Yun Shen | CAREERS, Northeast, ACCESS CSSN, CCMNet | ||
Yongwook Song | Kentucky |
Logo | Name | Description | Tags | Join |
---|---|---|---|---|
Large Data Sets | For people who evaluate or use storage options for researchers with large data sets. | Login to join |
Title | Date |
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CI Pathways: Leading the Way to Effective CI Use | 02/20/25 |
Globus and ACCESS Simplify Data Management for Supercomputer Users | 09/14/23 |
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.
Massachusetts Green High Performance Computing Center
Campus Champions, Northeast
student-facilitator, student champion
ACCESS CSSN
mentor, researcher/educator, research computing facilitator, research software engineer, cssn
University of California - Riverside
Campus Champions
research computing facilitator
Worcester Polytechnic Institute
Northeast
student-facilitator, regional facilitator, Masquerade