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monte-carlo

Mentors and Regional Facilitators
Name Region Skills Interests
Andrew Fullard Campus Champions
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 ACCESS CSSN
Thomas Langford Campus Champions, CAREERS
Lonnie Crosby Campus Champions, ACCESS CSSN
Michael Puerrer Campus Champions, Northeast
Patrick Burns RMACC
Paul Rulis Campus Champions
Renos Zabounidis Campus Champions
Ron Rahaman Campus Champions
Xiaoge Wang Campus Champions
Users
Name Roles Skills Interests
Carlos Paniagua
researcher/educator
Balamurugan Desinghu
mentor
researcher/educator
rcf
Hyungil Kye
student facilitator
Jason Smith
mentor
Thomas Langford
mentor
rcf
Dylan Le
student facilitator
Nithila Arunkumar
student facilitator
Parameshwaran …
student facilitator
Shagesh Sridharan
student facilitator
Stanley Nwoji
researcher/educator
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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

Balamurugan Desinghu

Rutgers, the State University of New Jersey

Programs

ACCESS CSSN, Campus Champions, CAREERS, Northeast

Roles

mentor, researcher/educator, research computing facilitator, cssn, Consultant

Bala Desinghu Photo

Expertise

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Lonnie Crosby

University of Tennessee, Knoxville

Programs

Campus Champions, ACCESS CSSN

Roles

researcher/educator, research computing facilitator, Affinity Group Leader, CIP

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Expertise

Cody Stevens

Wake Forest University

Programs

Campus Champions

Roles

regional facilitator, researcher/educator, research computing facilitator, ci systems engineer

Cody Stevens

Expertise

People with Interest

Christina Koch

University of Wisconsin-Madison

Programs

ACCESS CSSN

Roles

research computing facilitator, cssn, Affinity Group Leader

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Interests

Justin Oelgoetz

Austin Peay State University

Programs

Campus Champions

Roles

mentor, researcher/educator, research computing facilitator

Placeholder headshot

Interests

Paul Rulis

University of Missouri-Kansas City

Programs

Campus Champions

Roles

researcher/educator, research computing facilitator

Paul Rulis

Interests