FreeSurfer Tutorials
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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.
DAGMan for orchestrating complex workflows on HTC resources (High Throughput Computing)
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DAGMan (Directed Acyclic Graph Manager) is a meta-scheduler for HTCondor. It manages dependencies between jobs at a higher level than the HTCondor Scheduler.
It is a workflow management system developed by the High-Throughput Computing (HTC) community, specifically for managing large-scale scientific computations and data analysis tasks. It enables users to define complex workflows as directed acyclic graphs (DAGs). In a DAG, nodes represent individual computational tasks, and the directed edges represent dependencies between the tasks. DAGMan manages the execution of these tasks and ensures that they are executed in the correct order based on their dependencies.
The primary purpose of DAGMan is to simplify the management of large-scale computations that consist of numerous interdependent tasks. By defining the dependencies between tasks in a DAG, users can easily express the order of execution and allow DAGMan to handle the scheduling and coordination of the tasks. This simplifies the development and execution of complex scientific workflows, making it easier to manage and track the progress of computations.
Advanced Compilers: The Self-Guided Online Course
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This is a self guided online course on compilers. The topics covered throughout the course include universal compilers topics like intermediate representations, data flow, and “classic” optimizations as well as more research focusedtopics such as parallelization, just-in-time compilation, and garbage collection.
Data Visualization Tools for Julia
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Plots.jl is the most widely used plotting library for the Julia programming language. It's known for being especially powerful in its versatility and intuitiveness. It's limited set of dependencies and wide applicability across different graphics packages make it especially helpful in visualizing the results of your latest Julia implementation.
However, there are still multiple options available for Julia programmers to visualize their datasets. The second link details a comparison against a variety of Julia packages.
Federated CI Resources
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Discussion about contributing cycles to the Open Science Grid.
Docker - Containerized, reproducible workflows
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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.
How to use Rclone
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Learn how to use Rclone to transfer data, specifically from your local drive to the Open Storage Network, vice versa.
Biopython Tutorial
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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.
GPU Computing Workshop Series for the Earth Science Community
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GPU training series for scientists, software engineers, and students, with emphasis on Earth science applications.
The content of this course is coordinated with the 6 month series of GPU Training sessions starting in Februrary 2022. The NVIDIA High Performance Computing Software Development Kit (NVHPC SDK) and CUDA Toolkit will be the primary software requirements for this training which will be already available on NCAR's HPC clusters as modules you may load. This software is free to download from NVIDIA by navigating to the NVHPC SDK Current Release Downloads page and the CUDA Toolkit downloads page. Any provided code is written specifically to build and run on NCAR's Casper HPC system but may be adapted to other systems or personal machines. Material will be updated as appropriate for the future deployment of NCAR's Derecho cluster and as technology progresses.
Thrust resources
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Thrust is a CUDA library that optimizes parallelization on the GPU for you. The Thrust tutorial is great for beginners. The documentation is helpful for anyone using Thrust.
Higher Ed Controlled Unclassified Information Slack (HigherEdCUI)
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Slack channel for the Higher Ed CUI community
Solving differential equations with Physics-informed Neural Network
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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.
Gaussian 16
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Gaussian 16 is a computational chemistry package that is used in predicting molecular properties and understanding molecular behavior at a quantum mechanical level.
Geocomputation with R (Free Reference Book)
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Below is a link for a book that focuses on how to use "sf" and "terra" packages for GIS computations. As of 5/1/2023, this book is up to date and examples are error free. The book has a lot of information but provides a good overview and example workflows on how to use these tools.
Setting up PyFR flow solver on clusters
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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.
Electric field analyses for molecular simulations
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Tool to compute electric fields from molecular simulations
C Programming
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"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.
Science Gateway Tool/Web App Template (Jupyter Notebook + ipywidgets)
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Use this template to turn any science gateway workflow into a web application!
Applications of Machine Learning in Engineering and Parameter Tuning Tutorial
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Slides for a tutorial on Machine Learning applications in Engineering and parameter tuning given at the RMACC conference 2019.
Regular Expressions
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Regular expressions (sometimes referred to as RegEx) is an incredibly powerful tool that is used to define string patterns for "find" or "find and replace" operations on strings, or for input validation. Regular Expressions are used in search engines, in search and replace dialogs of word processors and text editors, and text-processing Linux utilities such as sed and awk. They are supported in many programming languages, including Python, R, Perl, Java, and others.
Set Up VSCode for Python and Github
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VSCode is a popular IDE that runs on Windows, MacOS, and Linux. This tutorial will explain how to get set up with VSCode to code in Python. It will also provide a tutorial on how to set up Github integration within VSCode.
Weka
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Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.
ACCESS HPC Workshop Series
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Monthly workshops sponsored by ACCESS on a variety of HPC topics organized by Pittsburgh Supercomputing Center (PSC). Each workshop will be telecast to multiple satellite sites and workshop materials are archived.
FSL Lectures
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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.
Benchmarking with a cross-platform open-source flow solver, PyFR
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What is PyFR and how does it solve fluid flow problems?
PyFR is an open-source Computational Fluid Dynamics (CFD) solver that is based on Python and employs the high-order Flux Reconstruction technique. It effectively solves fluid flow problems by utilizing streaming architectures, making it suitable for complex fluid dynamics simulations.
How does PyFR achieve scalability on clusters with CPUs and GPUs?
PyFR achieves scalability by leveraging distributed memory parallelism through the Message Passing Interface (MPI). It implements persistent, non-blocking MPI requests using point-to-point (P2P) communication and organizes kernel calls to enable local computations while exchanging ghost states. This design approach allows PyFR to efficiently operate on clusters with heterogeneous architectures, combining CPUs and GPUs.
Why is PyFR valuable for benchmarking clusters?
PyFR's exceptional performance has been recognized by its selection as a finalist in the ACM Gordon Bell Prize for High-Performance Computing. It demonstrates strong-scaling capabilities by effectively utilizing low-latency inter-GPU communication and achieving strong-scaling on unstructured grids. PyFR has been successfully benchmarked with up to 18,000 NVIDIA K20X GPUs on Titan, showcasing its efficiency in handling large-scale simulations.