Knowledge Base Resources

Training Resources and Courses offered by Texas A&M's Research Computing Group

A couple of resources that: 1.) Presents and defends a git branching workflow for stable collaborative git based projects. ("A Successful Git Branching Model") 2.) Maps "What do you want to do?" to the commands necessary to accomplish it. ("Git Flight Rules")

InsideHPC is an informational site offers videos, research papers, articles, and other resources focused on machine learning and quantum computing among other topics within high performance computing.

Materials for the "OpenHPC: Beyond the Install Guide" half-day tutorial, first offered at PEARC24. The goal of this repository is to let instructors or self-learners to construct one or more OpenHPC 3.x virtual environments, for those environments to be as close as possible to the defaults from the OpenHPC installation guide, and to then use those environments to demonstrate several topics beyond the basic installation guide. Topics include: 1. Building a login node that's practically identical to a compute node (except for where it needs to be different) 2. Adding more security to the SMS and login node 3. Using node-local storage for the OS and/or scratch 4. De-coupling the SMS and the compute nodes (e.g., independent kernel versions) 5. GPU driver installation (simulated/recorded, not live) 6. Easier management of node differences (GPU or not, diskless/single-disk/multi-disk, Infiniband or not, etc.) 7. Slurm configuration to match some common policy goals (fair share, resource limits, etc.)

Data visualization is a critical aspect of data analysis. It allows for a clear and concise representation of data, making it easier for users to understand and interpret complex datasets. One of the most popular libraries for data visualization in Python is Matplotlib. The included website aims to provide a brief overview of Matplotlib, its features, and examples/exercises to dive deeper into its functionalities.

A comprehensive collection of NERSC developed training and tutorial events, offered on regular schedules. All sessions are archived, including slide decks, video recordings, and software examples as are available. Some examples of past training and tutorial topics are listed below Deep Learning for Sciences Webinar Series BerkeleyGW Tutorial Workshop VASP Trainings Timemory Software Monitoring Tutorial, April 2021 HPCToolkit to Measure and Analyzing GPU Applications Performance Tutorial Totalview Tutorial NVidia HPCSDK - OpenMP Target Offload Training Parallelware Training Series ARM Debugging and Profiling Tools Tutorial Roofline on NVIDIA GPUs GPUs for Science events 3-part OpenACC Training Series 9-part CUDA Training Series

This tutorial introduces the use of Containers using the Charliecloud software suite. This tutorial will provide participants with background and hands-on experience to use basic Charliecloud containers for HPC applications. We discuss what containers are, why they matter for HPC, and how they work. We'll give an overview of Charliecloud, the unprivileged container solution from Los Alamos National Laboratory's HPC Division. Students will learn how to build toy containers and containerize real HPC applications, and then run them on a cluster. Exercises are demonstrated using the ACES cluster, a composable accelerator testbed at Texas A&M University. Students with an allocation on the ACES cluster can follow along with the ACES-specific exercises.

Representation learning is a fundamental concept in machine learning and artificial intelligence, particularly in the field of deep learning. At its core, representation learning involves the process of transforming raw data into a form that is more suitable for a specific task or learning objective. This transformation aims to extract meaningful and informative features or representations from the data, which can then be used for various tasks like classification, clustering, regression, and more.

Introductory video about DELTA. Speaker Tim Boerner, Senior Assistant Director, NCSA

5 Days of recordings of Python data analysis and visualization training.

Documentation for Anvil, a powerful supercomputer at Purdue University that provides advanced computing capabilities to support a wide range of computational and data-intensive research spanning from traditional high-performance computing to modern artificial intelligence applications.

Resources and User Guide available at Rockfish

Gaussian 16 is a computational chemistry package that is used in predicting molecular properties and understanding molecular behavior at a quantum mechanical level.

HPCwire is a prominent news and information source for the HPC community. Their website offers articles, analysis, and reports on HPC technologies, applications, and industry trends.

Some examples for writing Thrust code. To compile, download the CUDA compiler from NVIDIA. This code was tested with CUDA 9.2 but is likely compatible with other versions. Before compiling change extension from thrust_ex.txt to thrust_ex.cu. Any code on the device (GPU) that is run through a Thrust transform is automatically parallelized on the GPU. Host (CPU) code will not be. Thrust code can also be compiled to run on a CPU for practice.

VisIt is a prominent open-source, interactive parallel visualization and graphical analysis tool predominantly used for viewing scientific data. Its GitHub repository offers a detailed insight into the software's source code, documentation, and contribution guidelines. In particular, it offers useful examples on how it

Python has become a very popular programming language and software ecosystem for work in Data Science, integrating support for data access, data processing, modeling, machine learning, and visualization. In this webinar, we will describe some of the key Python packages that have been developed to support that work, and highlight some of their capabilities. This webinar will also serve as an introduction and overview of topics addressed in two Cornell Virtual Workshop tutorials, available at https://cvw.cac.cornell.edu/pydatasci1 and https://cvw.cac.cornell.edu/pydatasci2

The Docker container library, commonly known as Docker Hub, is a vast repository that hosts a multitude of pre-configured container images, streamlining the deployment process. It can drastically speed up a workflow, and gives you a consistent starting point each time. Check it out, they might have exactly what you are looking for!

Often when working with GIS, or spatial data, one encounters the word "datum" and it may require that you choose a "datum" when doing GIS computation tasks. Below is a short video on what are datums from NOAA and UCAR.

This documentation provides an overview of the PetIGA framework, an open source code for solving multiphysics problems with isogeometric analysis. The documentation covers some simple tutorials and examples to help users get started with the framework and apply it to solve real-world problems in continuum mechanics, including solid and fluid mechanics.

Mathematical optimization deals with the problem of finding numerically minimums or maximums of a functions. This tutorial provides the Python solutions for the optimization problems with examples.

The NSF-funded ResearchSOC helps make scientific computing resilient to cyberattacks and capable of supporting trustworthy, productive research through operational cybersecurity services, training, and information sharing necessary to a community as unique and variable as research and education (R&E). ResearchSOC is a service offering from Indiana University's OmniSOC.

This is a short video on how to exchange ACCESS credits and connect to Jetstream 2 (please note this was created for Duke users but applies to all) .

The "Fairness and Machine Learning" book offers a rigorous exploration of fairness in ML and is suitable for researchers, practitioners, and anyone interested in understanding the complexities and implications of fairness in machine learning.