A book for researchers who contribute code to R projects: This booklet is the result of my work with the Social Cognition for Social Justice lab. It was developed in response to questions I was getting from students; both grad students that were making software design decisions, and undergraduates who were using things like version control for the first time. Although many tutorials and resources exist for these topics, there was not a single source that I thought covered just enough material to build up to the workflow used by the lab without extraneous detail.

Conda is a popular package management system. This tutorial introduces you to Conda and walks you through managing Python, your environment, and packages.

Pluses and challenges of mentor selection. Offers tips for acquiring a mentor (finding, asking). And how to be a good mentee. SMART framework mentioned. Discrimination mentioned. Difference between mentor and sponsor underlined. More than one mentor encouraged. Good tips.

Warewulf is an operating system provisioning platform for Linux that is designed to produce secure, scalable, turnkey cluster deployments that maintain flexibility and simplicity. It can be used to setup a stateless provisioning in HPC environment.

Astropy is a community-driven package that offers core functionalities needed for astrophysical computations and data analysis. From coordinate transformations to time and date handling, unit conversions, and cosmological calculations, Astropy ensures that astronomers can focus on their research without getting bogged down by the intricacies of programming. This guide walks you through practical usage of astropy from CCD data reduction to computing galactic orbits of stars.

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.)

R GIS packages "rgdal", "rgeos", and "maptools" are package set to be archived and no longer supported by end of 2023. Many other R GIS packages are build on top of these packages, including "sp" and "raster". The packages recommended as replacement for "sp" is "sf" and the replacement for "raster" is "terra". Below are links to published articles regarding this transition. Additionally, I am including links to the documentation for the new packages recommended to be used "sf" and "terra".

The Biopython Tutorial and Cookbook website is a dedicated online resource for users in the field of computational biology and bioinformatics. It provides a collection of tutorials and practical examples focused on using the Biopython library. The website offers a series of tutorials that cover various aspects of Biopython, catering to users with different levels of expertise. It also includes code snippets and examples, and common solutions to common challenges in computational biology.

Neocortex is a new supercomputing cluster at the Pittsburgh Supercomputing Center (PSC) that features groundbreaking AI hardware from Cerebras Systems.

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

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.

Scikit-learn is free software machine learning library for Python. It has a variety of features you can use on data, from linear regression classifiers to xg-boost and random forests. It is very useful when you want to analyze small parts of data quickly.

The following link elaborates the usage of OpenMP API and its related syntax. There are also several exercises available for learners to help them get familiar with this widely-used tool for multi-threaded realization.

High Performance Computing (HPC) Cluster

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.

This is the official University of Oxford FSL group lecture page. This includes information on upcoming and past courses (online and in-person), as well as lecture materials. Available lecture materials includes slides and recordings on using FSL, MR physics, and applications of imaging data.

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!

Goes through in detail on how to build an application that can run on Android and IOS devices, using Qt Creator to develop Qt Quick applications. Goes through the setting up, creation, configuration, optimization, and overall deployment. This provides the fundamental basis, need to click around on the site for more specifics.

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.

Slides for a tutorial on Machine Learning applications in Engineering and parameter tuning given at the RMACC conference 2019.

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.

CS244N is a renowned natural language processing course offered by Stanford University and taught by Christopher Manning. It covers a wide range of topics in NLP, including language modeling, machine translation, sentiment analysis, and more. It teaches both foundational concepts and cutting-edge research to gain a comprehensive understanding of NLP techniques and applications.

DeepChem is an open-source library built on TensorFlow and PyTorch. It is helpful in applying machine learning algorithms to molecular data.

An overview of tools and methods to manage and optimize jobs and HPC workflows