Knowledge Base Resources

An introduction to Cloud Computing

This video by the YouTube channel 3Blue1Brown provides a very simplified introduction to the theory behind neural networks. This tutorial is perfect for those that don't have much linear algebra or machine learning background and are eager to step into the realm of ML!

Expanse at SDSC is a cluster designed by Dell and SDSC delivering 5.16 peak petaflops, and offers Composable Systems and Cloud Bursting.

Hour of Cyberinfrastructure (Hour of CI) is a nationwide campaign to introduce undergraduate and graduate students to cyberinfrastructure and geographic information science (GIS).

These instructions were executed on the FASTER and Grace cluster computing facilities at Texas A&M University. However, the process can be applied to other clusters with similar environments. For local installation, please refer to the PyFR documentation. Please note that these instructions were valid at the time of writing. Depending on the time you're executing these, the versions of the modules may need to be updated. 1. Loading Modules The first step involves loading pre-installed software libraries required for PyFR. Execute the following commands in your terminal to load these modules: module load foss/2022b module load libffi/3.4.4 module load OpenSSL/1.1.1k module load METIS/5.1.0 module load HDF5/1.13.1 2. Python Installation from Source Choose a location for Python 3.11.1 installation, preferably in a .local directory. Navigate to the directory containing the Python 3.11.1 source code. Then configure and install Python: cd $INSTALL/Python-3.11.1/ ./configure --prefix=$LOCAL --enable-shared --with-system-ffi --with-openssl=/sw/eb/sw/OpenSSL/1.1.1k-GCCcore-11.2.0/ PKG_CONFIG_PATH=$LOCAL/pkgconfig LDFLAGS=/usr/lib64/libffi.so.6.0.2 make clean; make -j20; make install; 3. Virtual Environment Setup A virtual environment allows you to isolate Python packages for this project from others on your system. Create and activate a virtual environment using: pip3.11 install virtualenv python3.11 -m venv pyfr-venv . pyfr-venv/bin/activate 4. Install PyFR Dependencies Several Python packages are required for PyFR. Install these packages using the following commands: pip3 install --upgrade pip pip3 install --no-cache-dir wheel pip3 install --no-cache-dir botorch pandas matplotlib pyfr pip3 uninstall -y pyfr 5. Install PyFR from Source Finally, navigate to the directory containing the PyFR source code, and then install PyFR: cd /scratch/user/sambit98/github/PyFR/ python3 setup.py develop Congratulations! You've successfully set up PyFR on the FASTER and Grace cluster computing facilities. You should now be able to use PyFR for your computational fluid dynamics simulations.

"These notes are part of the UW Experimental College course on Introductory C Programming. They are based on notes prepared (beginning in Spring, 1995) to supplement the book The C Programming Language, by Brian Kernighan and Dennis Ritchie, or K&R as the book and its authors are affectionately known. (The second edition was published in 1988 by Prentice-Hall, ISBN 0-13-110362-8.) These notes are now (as of Winter, 1995-6) intended to be stand-alone, although the sections are still cross-referenced to those of K&R, for the reader who wants to pursue a more in-depth exposition." C is a low-level programming language that provides a deep understanding of how a computer's memory and hardware work. This knowledge can be valuable when optimizing apps for performance or when dealing with resource-constrained environments.C is often used as the foundation for creating cross-platform libraries and frameworks. Learning C can allow you to develop libraries that can be used across different platforms, including iOS, Android, and desktop environments.

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.

In GIS, projections are helpful to take something plotted on a globe and convert it to a flat map that we can print or show on a screen. Unfortunately it also introduces distortions to the objects and features on the map. This not only distorts the objects visually, but the results for any spatial attribute calculations will also reflect this distortion (such as distance and area ). Below is a link to a quick primer on projections, types of distortions that can occur, and suggestions on how to choose a correct projection for your work.

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.

Offers comprehensive information on various master's degree options in cybersecurity, including program details, admission requirements, and career opportunities, helping students make informed decisions about pursuing an advanced degree in cybersecurity.

This website summarizes the notes of Stanford's introductory course on probabilistic graphical models. It starts from the very basics and concludes by explaining from first principles the variational auto-encoder, an important probabilistic model that is also one of the most influential recent results in deep learning.

Differential equations, the backbone of countless physical phenomena, have traditionally been solved using numerical methods or analytical techniques. However, the advent of deep learning introduces an intriguing alternative: Physics-Informed Neural Networks (PINNs). By leveraging the representational power of neural networks and integrating physical laws (like differential equations), PINNs offer a novel approach to solving complex problems. This guide walks through an implementation of a PINN to solve DEs such as the logistic equation.

As developers, we get excited to think about challenging problems. When you ask us what we are working on, our eyes light up like children in a candy store. So why is it that so many of our developer and software origin stories are not told? How did we get to where we are today, and what did we learn along the way? This podcast aims to look “Behind the Scenes of Tech’s Passion Projects and People.” We want to know your developer story, what you have built, and why. We are an inclusive community - whatever kind of institution or country you hail from, if you are passionate about software and technology you are welcome!

This tutorial introduces machine learning on high performance computing (HPC) clusters. While it focuses on the HPC clusters at The University of Arizona, the content is generic enough that it can be used by students from other institutions.

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

The free online book for the mlr3 machine learning framework for R. Gives a comprehensive overview of the package and ecosystem, suitable from beginners to experts. You'll learn how to build and evaluate machine learning models, build complex machine learning pipelines, tune their performance automatically, and explain how machine learning models arrive at their predictions.

OnShape FeatureScripts allow users to create their own features via OnShape's programming language. The user can make these as simple or complex as they need, and they can save tons of time for heavy OnShape users or complex projects!

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.

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

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.

This slices and videos introduced how to use K-Nearest-Neighbors method to impute climate data and how to use Bayesian Spatio-Temporal models in R-INLA to impute mortality data. The demos will be added soon.

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

MDAnalysis is a python based library of tools for the analysis of molecular dynamics simulations. It is able to read and write many popular simulation formats including CHARMM, LAMMPS, GROMACS, and AMBER and more. This link contains the documentation pages of all MDAnalysis functions and has links to tutorials using Jupyter Notebooks.

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