Knowledge Base Resources

This code showcases how to work with the header-only nlohmann JSON library for C++. In order to compile, change the extensions from json_test.txt to json_test.cpp and test.txt to test.json. You must also download the header files from https://github.com/nlohmann/json. Complilation instructions are at the bottom of json_test. This code is very helpful for creating config files, for example.

Comprehensive tutorials and lecture notes covering various aspects of scientific computing using Python and Scipy.

EasyBuild is a software installation framework that allows administrators to easily build and install software on high-performance computing (HPC) systems. It supports a wide range of software packages, toolchains, and compilers. Supported software are found in the EasyConfigs repository, one of several resositories in EasyBuild project.

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.

Upcoming training events and archives of training materials detailing general HPC best practices as well as how to use OLCF resources and services.

A tutorial paper that presents a generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph. Following a single, simple computational rule, the sum-product algorithm computes either exactly or approximately various marginal functions derived from the global function. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sum-product algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's (1988) belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform (FFT) algorithms

Docker allows for containerization of any task - basically a smaller, scalable version of a virtual machine. This is very useful when transferring work across computing environments, as it ensures reproducibility.

A library of short videos about ACCESS allocations, resources and support.

Proxmox Virtual Environment is a hyper-converged infrastructure open-source software. It is a hosted hypervisor that can run operating systems including Linux and Windows on x64 hardware.

Describes effective mentorship (both ways).

Connect.Cybinfrastructure is a family of portals, each representing a program that is serving a segment of the research computing and data community. Each portal provides program-specific information, as well a custom "view" into a common database. The portal was originally developed to support project workflows and a knowledge base of self service learning resources for the Northeast Cyberteam. Subsequently, it was expanded to provide support to multiple cyberteams and other research computing communities of practice. We welcome additional communities, please contact us if you are interested in participating. Central to the Portal is an extensive and ever-evolving tagging infrastructure which informs every aspect of the Portal. The tag taxonomy was initially developed by the Northeast Cyberteam to categorize subject matter relevant to practitioners of Research Computing Facilitation and is ever changing due to the frequent introduction of new technology in domains that characterize the field of research computing.

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

This link is a documentary website to use AHPCC.

The official documentation for Python 3.11.5. Python comes with a lot of features built into the language, so it is worth taking a look as you code.

With the recent uprising of LLM's many business are looking at way to adopt these LLMs and fine-tuning these models on specfic data sets to ensure accuracy. These models when fine-tuned can be optimal for fulfilling the specific needs of a company. This site explains explicitly when, how, and why models should be trained. It goes over various strategies for LLM fine -tuning.

This textbook is the first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines including computational neurosciences, machine learning, artificial intelligence, and robotics. It was published in 2022 and it's open access at this time. The contents in this textbook should be educational to those who want to understand how the free energy principle is applied to the normative behavior of living organisms and who want to widen their knowledge of sequential decision making under uncertainty.

A class from MITOpenCourseware that gives a hands on approach to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, caching optimizations, parallel programming, and building scalable systems.

Spatial Data Science is a growing field across a wide range of industries and disciplines. The open-source programming language Python has many libraries that support spatial analysis, but what do you do when your computer is unable to tackle the massive file sizes of high-resolution data and the computing power required in your analysis? There materials have been prepared to teach you spatial data science and how to execute your analysis using a high-performance computer (HPC).

Very helpful list of external resources from Trusted CI

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.

Machine learning is becoming increasingly important in field with large data such as astrophysics. AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib, and astropy allowing for a range of statistical and machine learning routines to analyze astronomical data in Python. In particular, it has loaders for many open astronomical datasets with examples on how to visualize such complicated and large datasets.

These slides provide an introduction on how Termius and Cursor, two new and freemium apps that use AI to perform more efficient work, can be used for faster HPC research.

The MOOSE Navier-Stokes Cahn-Hilliard (NSCH) application is a library for implementing simulation tools that solve the Navier-Stokes Cahn-Hilliard equations with non-matching densities using Galerkin finite element methods with a residual-based stabilization scheme.