Name | Region | Skills | Interests |
---|---|---|---|
Adrian Del Maestro | Northeast | ||
Anita Schwartz | Campus Champions | ||
Andrew Sherman | ACCESS CSSN, Campus Champions, CAREERS | ||
Chris Carothers | CAREERS | ||
Cody Stevens | Campus Champions | ||
Balamurugan Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
Daniel Howard | RMACC, Campus Champions, ACCESS CSSN | ||
Edwin Posada | Campus Champions | ||
Gaurav Khanna | Campus Champions, CAREERS, Northeast | ||
Craig Gross | Campus Champions | ||
Jacob Pessin | Northeast | ||
Katia Bulekova | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
Marina Kraeva | Campus Champions | ||
Thomas Langford | Campus Champions, CAREERS | ||
Lonnie Crosby | Campus Champions | ||
Martin Cuma | RMACC, Campus Champions | ||
Michael Puerrer | Campus Champions, Northeast | ||
Justin Oelgoetz | Campus Champions | ||
Paul Rulis | Campus Champions | ||
Ron Rahaman | Campus Champions | ||
Sean Anderson | Campus Champions | ||
Grant Scott | Great Plains | ||
Xiaoqin Huang | ACCESS CSSN | ||
Shaohao Chen | Northeast | ||
Swabir Silayi | Campus Champions | ||
Seung Woo Son | Northeast | ||
William Lai | ACCESS CSSN |
Name | Roles | Skills | Interests |
---|---|---|---|
Andrew Sherman |
mentor rcf steering committee |
||
Chris Carothers |
mentor steering committee |
||
Balamurugan Desinghu |
mentor researcher/educator rcf |
||
Gaurav Khanna |
mentor regional facilitator researcher/educator rcf steering committee |
||
Katia Bulekova |
mentor rcf |
||
Thomas Langford |
mentor rcf |
||
Parameshwaran … |
student facilitator |
||
Sanguthevar Ra… | researcher/educator |
Project Title | Project Institution Sort descending | Project Owner | Tags | Status |
---|---|---|---|---|
Ultrafast Spectral Energy Distribution Modeling of Galaxies using GPUs | Siena College | John Moustakas | optimization, parallelization, astrophysics, gpu, python | Reviewing Applicants |
Expanding computational resources for gravitational wave detection pipelines in medium-latency and beyond | University of Rhode Island | Gaurav Khanna | Analysis and Algorithms, astrophysics, benchmarking, data-transfer, gravitational-waves, parallelization, python, scheduling | In Progress |
An optimized search algorithm for gravitational waves from post-merger remnants | University of Rhode Island | Gaurav Khanna | astrophysics, conda, cuda, gpu, parallelization, python | Complete |
Title | Date |
---|---|
COMPLECS: Parallel Computing Concepts | 5/02/24 |
Title | Category | Tags | Skill Level |
---|---|---|---|
Numba: Compiler for Python | Docs | vectorization, optimization, performance-tuning, parallelization | Intermediate, Advanced |
Bioinformatics Workflow Management with Nextflow | Docs | cloud-computing, parallelization, data-management, bioinformatics, training | Beginner, Intermediate |
MPI Resources | Learning | parallelization, mpi | Beginner, Intermediate |
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.
Yale University
Campus Champions, CAREERS
mentor, research computing facilitator
University of Southern California
ACCESS CSSN, Campus Champions
mentor, research computing facilitator