Eighth Annual Texas A&M Research Computing Symposium

Last Update: May 11, 2025

Symposium Details

Dates: May 12-14, 2025
Location: Talks and Workshops - Texas A&M Innovative Learning Classroom Building (ILCB), College Station, TX (map)
Parking: Paid parking is available at the Stallings Blvd Garage (SBG) adjacent to the ILCB building (map)
Questions? Call us at (979) 458-8414 or email events@hprc.tamu.edu

To attend the Symposium Talks and Special Session use the Symposium Registration:

To attend the Symposium Workshops, use the Workshop Registration:

Texas A&M's High Performance Research Computing is hosting a series of talks and Workshops May 12-14, 2025 to showcase the A&M community’s work in high-performance computing and data-intensive research.

You are invited and encouraged to participate as a speaker or poster presenter to share your computationally challenging research with the community. The submission deadline to for full consideration as a speaker or poster presenter is April 25.

The Symposium will feature a keynote presentation on the National Artificial Intelligence Research Resource (NAIRR) Pilot and a presentation and training for the upcoming Texas A&M University System DGX SuperPOD featuring 760 NVIDIA H200 GPUs along with a special lunch session to discuss early access to the DGX SuperPOD.

Check back regularly for updated details on speakers, workshops, special sessions and the agenda.

Keynote Talk:

Katie Antypas, Director of the National Science Foundation's (NSF) Office of Advanced Cyberinfrastructure (OAC)

Presentation on the upcoming Texas A&M University System DGX SuperPOD featuring 760 H200 GPUs (Monday)

Special lunch session on early access to the DGX SuperPOD (Tuesday)

Banquet and Poster Session with a prize for best poster (Monday)

Speakers

Prof. Phanourios Tamamis, Chemical Engineering, Texas A&M University
Dr. Honggao Liu, High Performance Research Computing, Texas A&M University
Prof. Wonmuk Hwang, Biomedical Engineering, Texas A&M University
Robert Lee, Dell Technologies Dr. Lars Koesterke, Texas Advanced Computing Center
Prof. Debjyoti Banerjee, Mechanical Engineering, Texas A&M University

Workshops on AI/ML, Data Science, AlphaFold3, Containers, MATLAB and CryoSPARC

NVIDIA Deep Learning Institute: Fundamentals of Accelerated Data Science, Instructor: Dr. Mahsa Lotfollahi, Solutions Architect, NVIDIA
AI TechLab in Jupyter Notebooks, Instructor: Dr. Zhenhua He, HPRC
Introduction to AlphaFold3 for 3D Protein Structure Prediction, Instructors: Dr. Michael Dickens, HPRC and Dr. Devon Boland, TIGGS
Introduction to CryoSPARC for Cryo-EM Data Processing, Instructors: Dr. Michael Dickens, HPRC and Dr. Gaya Yadav, LBSD
Using MATLAB on the ACES Cluster, Instructor: Marinus Pennings, HPRC
Fundametals of Containers, Instructor: Richard Lawrence, HPRC

Keynote Talk

The National AI Research Resource (NAIRR) Pilot

Katie Antypas, Director of the National Science Foundation's (NSF) Office of Advanced Cyberinfrastructure (OAC)

Bio: Katie is the Director of the NSF's Office of Advanced Cyberinfrastructure which is responsible for supporting and coordinating the development and deployment of advanced computing and data research infrastructure, tools, services, and training for the research and education community. Prior to joining NSF Katie was at the National Energy Research Scientific Computing (NERSC) Center at Lawrence Berkeley National Laboratory for 17 years in a variety of roles including NERSC Division Deputy, Project Director for NERSC's large scale High Performance Computing system acquisitions, Director of Hardware and Integration of the Exascale Computing Project, Data Department Head and User Services Group Lead. Before coming to NERSC in 2006, Katie worked at the Flash Center at the University of Chicago on the FLASH code, a highly scalable, parallel, adaptive mesh refinement astrophysics application. She has an M.S. in Computer Science from the University of Chicago and a bachelors in Physics from Wellesley College.

Workshops

AI TechLab in Jupyter Notebooks

Tuesday, May 13, 1:00 PM - 4:00 PM

Instructor: Dr. Zhenhua He, Texas A&M High Performance Research Computing

This technology lab contains a set of sessions to help a new user start an AI project on the ACES cluster, a composable accelerator testbed at Texas A&M University. You will learn how to create and activate virtual environment, manipulate and visualize data with Pandas and Matplotlib, use Scikit-learn for linear regression and classification applications, and use Pytorch to create and train a simple image classification model with deep neural networks (DNN).

• Access the ACES cluster
• Learn to use JupyterLab app on ACES OpenOnDemand (OOD) portal
• Learn to load software modules and create virtual environment for AI/ML projects
• Learn two Python libraries (Pandas and Matplotlib) for data science
• Learn fundamentals of AI/ML
• Learn how to use the scikit-learn and keras libraries for ML and DL applications.

• Create and activate a virtual environment and run JupyterLab on the HPRC Portal.
• Go through simple examples with two popular Python modules: Pandas and Matplotlib
• Learn to use scikit-learn for linear regression and classification applications.
• Learn how to use Pytorch to create and train a simple image classification model with deep neural networks (DNN).

• Current ACCESS ID
• Basic Python skills

Introduction to AlphaFold3 for 3D Protein Structure Prediction

Tuesday, May 13, 1:00 PM - 4:00 PM

Instructors: Dr. Michael Dickens, Texas A&M High Performance Research Computing and Dr. Devon Boland, Texas A&M Institute for Genome Sciences & Society

In this workshop, participants will learn about how to run AlphaFold3 and the fundamentals of AlphaFold as applied to real world applications.

• AlphaFold resources and limitations
• Shared database files
• Run ParaFold to reduce GPU idle time
• Submit an example ParaFold/AlphaFold2 job script
• Submit an example AlphaFold3 job script
• Visualize and interpret AlphaFold job results

• AlphaFold history
• Running AlphaFold2 on ACES: ParaFold workflow, reduced_dbs, and confidence metrics
• Running AlphaFold3 on ACES
• HPRC Cluster Utilities
• Visualization of Results: View predictions in Jmol
• Job Resource Monitoring
• Real world applications

• Active ACCESS ID
• Basic Linux/Unix skills
• Download the AlphaFold3 model parameters file: https://github.com/google-deepmind/alphafold3 Requests to download the parameters can take 1 or more business days.

Fundamentals of Accelerated Data Science, NVIDIA Deep Learning Institute (DLI)

Wesdnesday, May 14, 9:00 AM - 5:00 PM

Instructor: Dr. Mahsa Lotfollahi, Solutions Architect, NVIDIA

In this Deep Learning Institute (DLI) workshop, developers will learn how to build and execute end-to-end single and multi-GPU accelerated data science workflows that enable them to quickly explore, iterate, and get their work into production. Using the RAPIDS accelerated data science libraries, developers will apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH’s single-source shortest path, and cuML’s KNN, DBSCAN, and logistic regression to perform data analysis at scale.

All participants who successfully complete the training will earn an NVIDIA DLI certificate to demonstrate your subject matter competency and support career growth.

• Use cuDF to accelerate pandas, Polars, and Dask for analyzing datasets of all sizes efficiently
• Utilize a wide variety of machine learning algorithms, including XGBoost, for different data science problems
• Deploy machine learning models on a Triton Inference Server to deliver optimal performance
• Learn and apply powerful graph algorithms to analyze complex networks with NetworkX and cuGraph
• Perform multiple analysis tasks on massive datasets to stave off a simulated epidemic outbreak effecting the UK
• Upon completion, you will be able to perform various data science tasks more efficiently, enabling more iteration cycles and drastically improving productivity.

Topics:

• Ingest and prepare several datasets (some larger-than-memory) for use in multiple machine learning exercises later in the workshop:
• Read data directly to single and multiple GPUs with pandas, Polars, cuDF, and Dask.
• Prepare population, road network, and clinic information for machine learning tasks on the GPU with cuDF.

• Use supervised and unsupervised GPU-accelerated algorithms with cuML.

• Create and analyze graph data on the GPU with cuGraph.

• Apply new GPU-accelerated data manipulation and analysis skills with population-scale data
• Use RAPIDS to integrate multiple massive datasets and perform real-world analysis.
• Pivot and iterate on your analysis as the simulated epidemic provides new data for each simulated day.

• Experience with Python, ideally including pandas and NumPy

Technologies: RAPIDS, cuDF, XGBoost, cuML, cuGraph, Dask, cuPy, pandas, Polars, NumPy, and Bokeh

Certificate: Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.

Introduction to CryoSPARC for Cryo-EM Data Processing in Collaboration with the Laboratory for Biomolecular Structure and Dynamics

Wesdnesday, May 14, 1:00 PM - 4:00 PM

Instructors: Dr. Michael Dickens, Texas A&M High Performance Research Computing and Dr. Gaya Yadav, Laboratory for Biomolecular Structure and Dynamics

This course will cover aspects of using CryoSPARC on HPRC resources including accessing CryoSPARC on the ACES cluster followed by a training session using example image files.

• How to launch a CryoSPARC job from the portal
• HPC resources and limitations
• Image file processing

• CryoSPARC on the ACES Portal
• Resources and Limitations
• Group data directories
• CryoSPARC training session

• Current ACCESS ID
• CryoSPARC Academic License ID. You must first get an individual academic license ID from https://cryosparc.com/download in order to launch CryoSPARC.

Using MATLAB on the ACES Cluster

Wesdnesday, May 14, 9:00 AM - 12:00 PM

Instructor: Marinus Pennings, Texas A&M High Performance Research Computing

This course introduces different ways to use MATLAB on the ACES cluster and how to leverage its parallel resources. Participants will use MATLAB on the portal through the interactive MATLAB App and learn how to create and submit MATLAB jobs using the Drona Composer. Other topics include how to start parallel pools and utilize GPUs on ACES.

• Using the MATLAB GUI with Open OnDemand
• Submitting MATLAB jobs using the Drona Composer
• Creating/managing cluster profiles
• Parallel programming using MATLAB workers
• Parallel programming using GPUs on ACES

• Current ACCESS ID
• Basic knowledge of MATLAB

Fundamentals of Containers

Wesdnesday, May 14, 1:00 PM - 4:00 PM

Instructor: Richard Lawrence, Texas A&M High Performance Research Computing

This course introduces core concepts of containerization and covers basic and advanced containerization tasks with a focus on understanding best practices for containers on HPC systems. The course is intended to cultivate an in-depth general-purpose understanding of containers for an audience who may be unfamiliar with containers or may have previously used containers only in a specific context. Topics include building containers images, working with container repositories, and container features necessary for using containers on HPC systems. Exercises will be performed on the ACES cluster, a composable accelerator testbed at Texas A&M University, using the Charliecloud and Singularity container engines.

• Use both Singularity and Charliecloud runtimes
• Retrieve a container image from an online repository
• Inspect container images and execute software found therein
• Build and modify containers by hand or from a recipe
• Save container images in HPC-friendly file formats

• Overview of containers
• Singularity and Charliecloud
• Getting a container image
• Container usage basics
• Building and modifying containers
• Containers for HPC

• Current ACCESS ID
• Basic Linux/Unix skills

Full Schedule (All times are CT), subject to change

Last Update: May 11, 2025

Thank you to our 2025 Sponsors!

Texas A&M Research Computing Symposium is supported in part by NSF award #2112356, ACSS: ACES - Accelerating Computing for Emerging Sciences.