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monte-carlo

Mentors and Regional Facilitators
Name Region Skills Interests
Aaron Jezghani Campus Champions
Michael Blackmon Campus Champions, ACCESS CSSN
Cody Stevens Campus Champions
Cesar Sul ACCESS CSSN, Campus Champions
Balamurugan Desinghu ACCESS CSSN, Campus Champions, CAREERS, Northeast
Fernando Garzon ACCESS CSSN
Georgia Stuart TRECIS
Jason Smith CAREERS
Jacob Fosso Tande Campus Champions
Jeffrey Weekley Campus Champions
Od Odbadrakh
Thomas Langford Campus Champions, CAREERS
Lonnie Crosby Campus Champions
Michael Puerrer Campus Champions, Northeast
Patrick Burns RMACC
Paul Rulis Campus Champions
Renos Zabounidis Campus Champions
Ron Rahaman Campus Champions

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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

Paul Rulis

University of Missouri-Kansas City

Programs

Campus Champions

Roles

researcher/educator, research computing facilitator

Paul Rulis

Expertise

Georgia Stuart

UT Dallas

Programs

TRECIS

Roles

mentor, researcher/educator

Placeholder headshot

Expertise

Michael Blackmon

Davidson College

Programs

Campus Champions, ACCESS CSSN

Roles

mentor, research computing facilitator, ci systems engineer, cssn

Placeholder headshot

Expertise

People with Interest

Darshan Sarojini

University of California, San Diego

Programs

ACCESS CSSN, Campus Champions

Roles

mentor, researcher/educator

Portrait

Interests

Cesar Sul

University of Southern California

Programs

ACCESS CSSN, Campus Champions

Roles

mentor, research computing facilitator

Picutre of Cesar Sul

Interests

Morgan Newton

Santa Monica College

Programs

ACCESS CSSN

Roles

student-facilitator

Picture of Morgan Newton

Interests