Using population genetic models to resolve and predict dispersal kernels of marine larvae
We are working with Penn State University and were awarded NSF award number OCE-2334798. The project represents the next step in the development of the Diversity of the Indo-Pacific Network (DIPnet), an NSF-funded Research Coordination Network created by the PIs and SPs to promote collaborative research on ecology and evolution in the biodiverse Indo-Pacific. Ultimately, this information will serve to develop a conservation portfolio approach in conjunction with local stakeholder engagement. The aim of the project is to understand the spatial distribution of planktonic dispersal events of tiny larvae by combining biophysical models, empirical theory and hydrodynamic information. We are submitting a request to use the ACCESS resources to support the modeling twenty years of hydrodynamics in the Indo-Pacific.
Unknown resources required - looking for the following software:
RGF-GRID
D-FlowFM Suite
Python
Bayesian nonparametric ensemble air quality model predictions at high spatio-temporal daily nationwide 1 km grid cell
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.