State of the Center Address
Dr. Dan Stanzione, Associate Vice President for Research at The University of Texas at Austin since 2018 and Executive Director of the Texas Advanced Computing Center (TACC) since 2014, is a nationally recognized leader in high performance computing. He is the principal investigator (PI) for several projects including a multimillion-dollar National Science Foundation (NSF) grant to acquire and deploy Frontera, which is the fastest supercomputer at a U.S. university. Dr. Stanzione is also the PI of TACC's Stampede2 and Wrangler systems, supercomputers for high performance computing and for data-focused applications, respectively. He served for six years as the co-director of CyVerse, a large-scale NSF life sciences cyberinfrastructure in which TACC is a major partner. In addition, Dr. Stanzione was a co-PI for TACC's Ranger and Lonestar supercomputers, large-scale NSF systems previously deployed at UT Austin. Dr. Stanzione received his bachelor's degree in electrical engineering and his master's degree and doctorate in computer engineering from Clemson University, where he later directed the supercomputing laboratory and served as an assistant research professor of electrical and computer engineering.
CPU, GPU, Arm, x86… Building Portable Software with NVIDIA HPC SDK
Dr. John Linford is a principal technical manager at NVIDIA focused on developing the CPU software ecosystem. John previously worked as the Arm director of HPC engineering. John has almost two decades of front-line HPC applications, systems, and optimization experience and particularly enjoys working with emerging technologies and extreme-scale systems. John is based in Austin, Texas.
Generative Modeling in Seismic Imaging
Yash Gandhi is a Machine Learning Engineer at SparkCognition where he works on the research team developing and implementing new models for a variety of customer problems. Mr. Gandhi's research interests span diffusion models, generative AI, computer vision, and reinforcement learning. Recently he's been working on implementing diffusion models for inverse problems, specifically those found in seismic imaging. Prior to joining SparkCognition, Mr. Gandhi graduated from University of Colorado with his Master's in Computer Science in 2020.
Space Weather and its Dependence on Supercomputing Resources
Dr. Yue Deng is a distinguished Professor in the Department of Physics at the University of Texas at Arlington. Dr. Deng’s research interests span many facets of space physics including 3-D modeling of complex systems, solar and geomagnetic energy input uncertainty into the upper atmosphere, gravity-acoustic wave propagation, and ionosphere-thermosphere coupling in multiple scales, among others. She has developed a new 3-D non-hydrostatic ionosphere / thermosphere general circulation model (GCM) for investigating the non-hydrostatic processes in the upper atmosphere. Additionally, Dr. Deng is leading multiple federally funded grants, she is a recipient of a NSF CAREER award, a member of the National Academy of Sciences (NAS) Committee of Solar and Space Physics (CSSP), and was named by NASA as one of three scientists to lead a Geospace Dynamics Constellation mission that will change our understanding of Earth’s upper atmosphere. She won the Joanne Simpson Medal of American Geophysics Union and became an AGU fellow in 2022.
Accelerating Discoveries with Large Language Models
Dr. Krishna Kumar is an Assistant Professor in Civil, Architecture, and Environmental Engineering at the University of Texas at Austin and Affiliated with the Oden Institute of Computational Science. Dr. Kumar’s research interests span high-performance computing, numerical modeling, explainable AI, and differentiable programming. Recently, his focus has been developing hybrid machine learning and differentiable programming to solve inverse problems and accelerate numerical simulations. Specifically, He has developed massively parallel micro-/macro-scale numerical methods including Graph Network Simulator, Material Point Method, Lattice Boltzmann - Discrete Element coupling, and Lattice Element method. Dr. Kumar was awarded C. S. Desai Award for the best paper on constitutive modeling of geologic materials by the Indian Geotechnical Society.
Back to TACCSTER Overview
In this tutorial we will focus on effectively leveraging the NSF-funded Tapis v3 Application Program Interface (API) for building a reproducible and trustworthy scientific machine learning workflow. The tutorial will include hands-on exercises, which will enable the attendees to develop Tapis applications based on an ML pipeline available from Hugging Face, that can be seamlessly moved to different execution environments, including a small virtual machine and a national-scale supercomputer.
High performance computing (HPC) systems serve a large role in academic computing at scale. In this hands-on tutorial, we will explore methods for running containers on HPC systems including advanced tasks like utilizing GPUs for computation and MPI (Message Passing Interface) for parallel jobs, as well as using Docker to perform multi-stage and multi-architecture builds.
Effective CUDA code differs from conventional code as it has to reflect and accommodate architectural features of GPUs. In this tutorial I will discuss several key elements of the hardware and the software, i.e. how the execution of a loop matches the grid and block structure of the hardware, how user-managed cache memory is leveraged for speed, and how data is transferred asynchronously from host to device. The tutorial is intended for intermediate-level C/C++ and Fortran programmers who are interested in making the first steps towards CUDA programming for GPUs.
This will be a 2-part tutorial. Part 1 is a demo of the UTRC portal and how you can use it for your research here at TACC. Part 2 is hands-on, setting up Cyberduck and how to upload your data to TACC.
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Olin Carty Hildebrand Department of Petroleum and Geosystems Engineering, and Center for Subsurface Energy and the Environment, The University of Texas at Austin
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Collin E. Haese Department of Mechanical Engineering, The University of Texas at Austin
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Dhanny Indrakusuma Texas Advanced Computing Center, The University of Texas at Austin
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Xu Liu William B. Hanson Center for Space Sciences, The University of Texas at Dallas
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Chintan Mehta Midwestern State University
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Andrea J. Nam Institute for Neuroscience, Center for Learning and Memory, and Department of Neuroscience, The University of Texas at Austin
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Joshua A. Osborne Department of Physics, The University of Texas at Arlington
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Jiwoo Park Chandra Department of Electrical and Computer Engineering, The University of Texas at Austin
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Lijun Qian CREDIT Center, Prairie View A&M University
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Snehal Tibrewal Center for Gravitational Physics and Department of Physics, The University of Texas at Austin
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Jonathan M. Towne Research Imaging Institute, and South Texas Medical Scientist Training Program, University of Texas Health Science Center at San Antonio
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Hugo Villar Castellanos The University of Texas Rio Grande Valley
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