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diana Trotman CAREERS
Diana Toups Dugas RMACC, SWEETER, Campus Champions
Elie Alhajjar ACCESS CSSN
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Amy Koshoffer Campus Champions
shuai liu ACCESS CSSN
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Engagements

Run Markov Chain Monte Carlo (MCMC) in Parallel for Evolutionary Study
Texas Tech University

My ongoing project is focused on using species trait value (as data matrices) and its corresponding phylogenetic relationship (as a distance matrix) to reconstruct the evolutionary history of the smoke-induced seed germination trait. The results of this project are expected to increase the predictability of which untested species could benefit from smoke treatment, which could promote germination success of native species in ecological restoration. This computational resources allocated for this project pull from the high-memory partition of our Ivy cluster of HPCC (Centos 8, Slurm 20.11, 1.5 TB memory/node, 20 core /node, 4 node). However, given that I have over 1300 species to analyze, using the maximum amount of resources to speed up the data analysis is a challenge for two reasons: (1) the ancestral state reconstruction (the evolutionary history of plant traits) needs to use the Markov Chain Monte Carlo (MCMC) in Bayesian statistics, which runs more than 10 million steps and, according to experienced evolutionary biologists, could take a traditional single core simulation up 6 months to run; and (2) my data contain over 1300 native species, with about 500 polymorphic points (phylogenetic uncertainty), which would need a large scale of random simulation to give statistical strength. For instance, if I use 100 simulations for each 500 uncertainty points, I would have 50,000 simulated trees. Based on my previous experience with simulations, I could design codes to parallel analyze 50,000 simulated trees but even with this parallelization the long run MCMC will still require 50000 cores to run for up to 6 months. Given this computational and evolutionary research challenge, my current work is focused on discovering a suitable parallelization methods for the MCMC steps. I hope to have some computational experts to discuss my project.

Status: In Progress

People with Expertise

Oriana Silva

University of North Texas

Programs

ACCESS CSSN

Roles

student-facilitator

Oriana Silva

Expertise

Eren Ada

University of Rhode Island

Programs

CAREERS

Roles

student-facilitator

Expertise

William Feng

Rutgers University-New Brunswick

Programs

CAREERS

Roles

student-facilitator

Placeholder headshot

Expertise

People with Interest

Oriana Silva

University of North Texas

Programs

ACCESS CSSN

Roles

student-facilitator

Oriana Silva

Interests

Xiaoqin Huang

Rice University

Programs

ACCESS CSSN

Roles

mentor, research computing facilitator, research software engineer, cssn

xqhuang at Rice

Interests

Kaitlyn Varela

Programs

ACCESS CSSN

Roles

student-facilitator

Photo of Kaitlyn Varela

Interests