• Easy MPI Tutorial
• Open MPI documentation

Workshop for beginners and intermediate students in MPI which includes helpful exercises. Open MPI documentation.

• ACES: Charliecloud Containers for Scientific Workflows (Video)
• ACES: Charliecloud Containers for Scientific Workflows (Slides)

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.

• Campus Champions Home

Campus Champions foster a dynamic environment for a diverse community of research computing and data professionals sharing knowledge and experience in digital research infrastructure.

• Open Storage Network

The Open Storage Network, a national resource available through the XSEDE resource allocation system, is high quality, sustainable, distributed storage cloud for the research community.

• Arkansas High Performance Computing Center

This link is a documentary website to use AHPCC.

• ACCESS KB Guide - DELTA

NCSA is the home of Delta, a computing and data resource that balances cutting-edge graphics processor and CPU architectures with a non-POSIX file system with a POSIX-like interface. Delta allows applications to reap the benefits of modern file systems without rewriting code.

• Gaussian 16 HomePage
• About Gaussian 16

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

• Displaying Scientific Data with Tableau

Tableau is a popular and capable software product for creating charts that present data and dashboards that allow you to explore data. It is typically used to present business or statistical data, but can also create compelling visualizations of scientific data. However, scientific data is often generated or stored in formats that are not immediately accessible by Tableau. This seminar will explore the data formats that work best with Tableau and the available mechanisms for generating scientific data in (or converting it to) those formats so that you can apply the full power of Tableau to create the best possible visualizations of your data.

• Vislt github
• Writing a File Format Reader

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

• Docker Container Library

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!

• Educause HEISC-800-171 Community Group

The purpose of this group is to provide a forum to discuss NIST 800-171 compliance. Participants are encouraged to collaborate and share effective practices and resources that help higher education institutions prepare for and comply with the NIST 800-171 standard as it relates to Federal Student Aid (FSA), CMMC, DFARS, NIH, and NSF activities.

• https://scipy-lectures.org/advanced/mathematical_optimization/

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.

• VS Code installation

Visual Studio Code, commonly known as VSCode, is a popular tool used by programmers worldwide. It serves as a text editor and an Integrated Development Environment (IDE) that supports a wide variety of programming languages. One of its key features is its extensive library of extensions. These extensions add on to the basic functionalities of VSCode, making coding more efficient and convenient. However, there's a catch. When these extensions are installed and used frequently, they generate a multitude of files. These files are typically stored in a folder named .vscode-extension within your home directory. On a cluster computing facility such as the FASTER and Grace clusters at Texas A&M University, there's a limitation on how many files you can have in your home directory. For instance, the file number limit could be 10000, while the .vscode-extension directory can hold around 4000 temporary files even with just a few extensions. Thus, if the number of files in your home directory surpasses this limit due to VSCode extensions, you might face some issues. This restriction can discourage users from taking full advantage of the extensive features and extensions offered by the VSCode editor. To overcome this, we can shift the .vscode-extension directory to the scratch space. The scratch space is another area in the cluster where you can store files and it usually has a much higher limit on the number of files compared to the home directory. We can perform this shift smoothly using a feature called symbolic links (or symlinks for short). Think of a symlink as a shortcut or a reference that points to another file or directory located somewhere else. Here's a step-by-step guide on how to move the .vscode-extension directory to the scratch space and create a symbolic link to it in your home directory: 1. Copy the .vscode-extension directory to the scratch space: Using the cp command, you can copy the .vscode-extension directory (along with all its contents) to the scratch space. Here's how: cp -r ~/.vscode-extension /scratch/user Don't forget to replace /scratch/user with the actual path to your scratch directory. 2. Remove the original .vscode-extension directory: Once you've confirmed that the directory has been copied successfully to the scratch space, you can remove the original directory from your home space. You can do this using the rm command: rm -r ~/.vscode-extension It's important to make sure that the directory has been copied to the scratch space successfully before deleting the original. 3. Create a symbolic link in the home directory: Lastly, you'll create a symbolic link in your home directory that points to the .vscode-extension directory in the scratch space. You can do this as follows: ln -s /scratch/user/.vscode-extension ~/.vscode-extension By following this process, all the files generated by VSCode extensions will be stored in the scratch space. This prevents your home directory from exceeding its file limit. Now, when you access ~/.vscode-extension, the system will automatically redirect you to the directory in the scratch space, thanks to the symlink. This method ensures that you can use VSCode and its various extensions without worrying about hitting the file limit in your home directory.

• Find Online Master's in Business Analytics

A degree in business analytics looks different in today’s world than it did a decade ago. In its most current application, business analytics uses modern data science and capabilities in machine learning (ML). The magic comes into play when these are leveraged for strategic planning.

• Using ACCESS at Duke

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) .

• What are Geodetic Datums?

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.

• University of Arizona Workshop Series: Introduction to HPC, Visualization

This workshop has an introduction to the concepts of visualization followed by hands on exercises. The concepts section has Speaker Notes, and the hands on section has an accompanying Jupyter notebook. The workshop is one in a series of Introduction to HPC

• FreeSurfer Tutorials

The official MGH / Harvard tutorial page for FreeSurfer. The FreeSurfer group has provided and designed a series of tutorials for using FreeSurfer and for getting acquainted with the concepts needed to perform its various modes of analysis and processing of MRI data. The tutorials are designed to be followed along in a terminal window where commands can be copy/pasted instead of typed.

• Why Mentoring Matters and How to Get Started

Describes effective mentorship (both ways).

• Containerization Explained

Containerization is a software development method in which applications are packaged into standard units for development, shipment, and deployment.

• Framework to help in scaling Machine Learning/Deep Learning/AI/NLP Models to Web Application level

This framework will help in scaling Machine Learning/Deep Learning/Artificial Intelligence/Natural Language Processing Models to Web Application level almost without any time.

• PyTorch Tutorial at Stanford University

This is a very barebones introduction to the PyTorch framework used to implement machine learning. This tutorial implements a feed-forward neural network and is taught completely asynchronously through Stanford University. A good start after learning the theory behind feed-forward neural networks.

• marimo | a next generation python notebook

Introduction seminar for new reactive python notebook from marimo ambassador.

• Regression forecasting and predicting - Practical Machine Learning Tutorial with Python p.5

This video series provides a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. It covers topics such as linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. Goes over the high level intuitions of the algorithms and how they are logically meant to work. Apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn.

• https://github.com/albumentations-team/albumentations

Data augmentation is a crucial step in the pipeline for image classification with deep learning. Albumentations is an extremely versatile Python library that can be used to easily augment images. Transformations include rotations, flips, downscaling, distortions, blurs, and many more. Citation: Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M, Kalinin AA. Albumentations: Fast and Flexible Image Augmentations. Information. 2020; 11(2):125. https://doi.org/10.3390/info11020125