HPC University
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A comprehensive list of training resources from the HPC University. HPCU is a virtual organization whose primary goal is to provide a cohesive, persistent, and sustainable on-line environment to share educational and training materials for a continuum of high performance computing environments that span desktop computing capabilities to the highest-end of computing facilities offered by HPC centers.
Cornell Virtual Workshop
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Cornell Virtual Workshop is a comprehensive training resource for high performance computing topics. The Cornell University Center for Advanced Computing (CAC) is a leader in the development and deployment of Web-based training programs. Our Cornell Virtual Workshop learning platform is designed to enhance the computational science skills of researchers, accelerate the adoption of new and emerging technologies, and broaden the participation of underrepresented groups in science and engineering. Over 350,000 unique visitors have accessed Cornell Virtual Workshop training on programming languages, parallel computing, code improvement, and data analysis. The platform supports learning communities around the world, with code examples from national systems such as Frontera, Stampede2, and Jetstream2.
Raftlib: Open Source library for concurrent data processing pipelines
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Raftlib is an open-source C++ Library that provides a framework for implementing parallel and concurrent data processing pipelines. It is designed to simplify the development of high-performance data processing applications by abstracting away the complexities of parallelism, concurrency, and data flow management.
It enables stream/data-flow parallel computation by linking parallel compute kernels together using simple right shift operators, similar to C++ streams for string manipulation. RaftLib eliminates the need for explicit usage of traditional threading libraries such as pthreads, std::thread, or OpenMP, which can lead to non-deterministic behavior when misused.
Astronomy data analysis with astropy
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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.
Quick and Robust Data Augmentation with Albumentations Library
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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
CMake Tutorials
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CMake is an open-source tool used to manage the build process in operating systems. This tutorial takes you through how to use CMake from the very basics with example projects.
Why 'N How: Martinos Center for Biomedical Imaging:
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The Why & How seminar series is designed to introduce research assistants, graduate students, and postdoctoral and clinical fellows – really, anyone who is interested – to the many tools used in medical imaging. These include software tools and most of the major imaging modalities wielded by investigators (MRI, PET, EEG, MEG, optical, TMS and others). As the name of the series suggests, the talks cover both the reasons researchers might need a particular tool and the nuts and bolts of how to apply it. You can watch videos of the overviews below.
Examples of code using JSON nlohmann header only Library for C++
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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.
MATLAB bioinformatics toolbox
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Bioinformatics Toolbox provides algorithms and apps for Next Generation Sequencing (NGS), microarray analysis, mass spectrometry, and gene ontology. Using toolbox functions, you can read genomic and proteomic data from standard file formats such as SAM, FASTA, CEL, and CDF, as well as from online databases such as the NCBI Gene Expression Omnibus and GenBank.
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.
Slurm User Group Mailing List
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Neural Networks in Julia
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Making a neural network has never been easier! The following link directs users to the Flux.jl package, the easiest way of programming a neural network using the Julia programming language. Julia is the fastest growing software language for AI/ML and this package provides a faster alternative to Python's TensorFlow and PyTorch with a 100% Julia native programming and GPU support.
Horovod: Distributed deep learning training framework
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Horovod is a distributed deep learning training framework. Using horovod, a single-GPU training script can be scaled to train across many GPUs in parallel. The library supports popular deep learning framework such as TensorFlow, Keras, PyTorch, and Apache MXNet.
What are LSTMs?
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This reading will explain what a long short-term memory neural network is. LSTMs are a type of neural networks that rely on both past and present data to make decisions about future data. It relies on loops back to previous data to make such decisions. This makes LSTMs very good for predicting time-dependent behavior.
NCSA HPC-Moodle
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Self-paced tutorials on high-end computing topics such as parallel computing, multi-core performance, and performance tools. Some of the tutorials also offer digital badges.
MATLAB with other Programming Languages
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MATLAB is a really useful tool for data analysis among other computational work. This tutorial takes you through using MATLAB with other programming languages including C, C++, Fortran, Java, and Python.
MDAnalysis - Python library for the analysis of molecular dynamics simulations
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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.
Examples of Thrust code for GPU Parallelization
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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.
OpenMP Tutorial
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OpenMP (Open Multi-Processing) is an API that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, FreeBSD, HP-UX, Linux, macOS, and Windows. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.
OnShape FeatureScripts: Custom features for everyone
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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!
Automated Machine Learning Book
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The authoritative book on automated machine learning, which allows practitioners without ML expertise to develop and deploy state-of-the-art machine learning approaches. Describes the background of techniques used in detail, along with tools that are available for free.
Implementing Markov Processes with Julia
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The following link provides an easy method of implementing Markov Decision Processes (MDP) in the Julia computing language. MDPs are a class of algorithms designed to handle stochastic situations where the actor has some level of control. For example, used at a low level, MDPs can be used to control an inverted pendulum, but applied in higher level decision making the can also decide when to take evasive action in air traffic management. MDPs can also be extended to the partially observable domain to form the Partially Observable Markov Decision Process (POMDP). This link contains a wealth of information to show one can easily implement basic POMDP and MDP algorithms and apply well known online and offline solvers.
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.
Texas A&M HPRC Training Site
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Training Resources and Courses offered by Texas A&M's Research Computing Group
R for Data Science
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R for Data Science is a comprehensive resource for individuals looking to harness the power of the R programming language for data analysis, visualization, and statistical modeling. Whether you're a beginner or an experienced data scientist, this guide will help you unlock the full potential of R in the realm of data science.