Knowledge Base Resources

These links take you to visualization resources supported by the University of Arizona's HPC visualization consultant (rtdatavis.github.io). The following links are specific to the Paraview program and the workflows that have been used my researchers at the U of Arizona. Some of the pages linked are very beginner friendly: getting started, working with cameras and keyframes for rendering, visualizing external files (netcdf climate data), graphs and data exporting. Many of the workflows involve using remote desktops via the Open On Demand interface, but if this isn't set up at your university you can use paraview locally on a desktop. Feel free to post on access ci https://ask.cyberinfrastructure.org/ if you need assistance getting a paraview gui open for your work on HPC.

This video series provides a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. It covers topics such as linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. Goes over the high level intuitions of the algorithms and how they are logically meant to work. Apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn.

A compilation of the slides from this year's RMACC Sys Admin Workshop. RMACC Sys Admin Workhop Schedule: Tuesday 12:00 PM Sign-in 1:00 PM Introductions 1:30 PM Lightning Talk - HPC Survival guide 2:00 PM Node Management - Scott Serr 2:30 PM Lightning Talk - Warewulf 3:00 PM Urgent HPC - Coltran Hophan-Nichols and Alexander Salois Wednesday 9:00 AM Breakfast 10:00 AM Round table Sites - BYU, INL, UMT, ASU, MSU 11:00 AM Open OnDemand setup - Dean Anderson 11:30 AM Lightning talk - Long term hardware support 12:00 PM Lunch 1:00 PM HPC Security - Matt Bidwell 2:00 PM Lightning talk- Security 2:30 PM ACCESS resources - Couso 3:00 PM Easybuild tutorial - Alexander Salois 3:30 PM General Q & A Thursday 9:00 AM Breakfast 10:00 AM Lightning Talk- Containers and Virtual Machines 11:00 AM University of Montana - Hellgate Site Tour 11:30 AM Closing Remarks

Training materials for using the bash (and zsh) shell.

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

Numba is a Python compiler designed for accelerating numerical and array operations, enabling users to enhance their application's performance by writing high-performance functions in Python itself. It utilizes LLVM to transform pure Python code into optimized machine code, achieving speeds comparable to languages like C, C++, and Fortran. Noteworthy features include dynamic code generation during import or runtime, support for both CPU and GPU hardware, and seamless integration with the Python scientific software ecosystem, particularly Numpy.

A lecture and notes with the goal of teaching introductory python. Starting by understanding how to download and start using python, then expanding to basic syntax for lists, arrays, loops, and methods.

Rocky Mountain Advanced Computing Consortium Website

Unix is incredibly common and useful. This website provides all the common commands and explanations for one to get started with a unix system.

Multi-threading guidance when using GDAL.

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.

This tutorial explains how to use Python for GPU acceleration with libraries like CuPy, PyOpenCL, and PyCUDA. It shows how these libraries can speed up tasks like array operations and matrix multiplication by using the GPU. Examples include replacing NumPy with CuPy for large datasets and using PyOpenCL or PyCUDA for more control with custom GPU kernels. It focuses on practical steps to integrate GPU acceleration into Python programs.

A tutorial on the effective use of Dask on HPC resources. The four-hour tutorial will be split into two sections, with early topics focused on novice Dask users and later topics focused on intermediate usage on HPC and associated best practices. The knowledge areas covered include (but are not limited to): Beginner section High-level collections including dask.array and dask.dataframe Distributed Dask clusters using HPC job schedulers Earth Science data analysis using Dask with Xarray Using the Dask dashboard to understand your computation Intermediate section Optimizing the number of workers and memory allocation Choosing appropriate chunk shapes and sizes for Dask collections Querying resource usage and debugging errors

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.

Documentation and research based on the latest NLP text generation detection methods for 2023.

Announcements for for users and developers of Charliecloud, which provides lightweight user-defined software stacks for high-performance computing.

Background: Big data, defined as having high volume, complexity or velocity, have the potential to greatly accelerate research discovery. Such data can be challenging to work with and require research support and training to address technical and ethical challenges surrounding big data collection, analysis, and publication. Methods: The present study was conducted via a series of semi-structured interviews to assess big data methodologies employed by CU Boulder researchers across a broad sample of disciplines, with the goal of illuminating how they conduct their research; identifying challenges and needs; and providing recommendations for addressing them. Findings: Key results and conclusions from the study indicate: gaps in awareness of existing big data services provided by CU Boulder; open questions surrounding big data ethics, security and privacy issues; a need for clarity on how to attribute credit for big data research; and a preference for a variety of training options to support big data research.

TensorFlow is a powerful framework for Deep Learning, developed by google. This specifically is their python package, which is easy to use and can be used to train incredibly powerful models.

Windows Subsystem for Linux (WSL) provides a Linux environment for Windows users to access HPC resources fast and efficiently.

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

Samtools is a suite of programs for interacting with high-throughput sequencing data, especially in the SAM/BAM format. It offers various utilities for processing, analyzing, and managing sequence data generated from next-generation sequencing (NGS) experiments. Samtools is widely used in bioinformatics and genomics research for tasks such as read alignment, variant calling, and data manipulation.

Neurodesk provides a containerised data analysis environment to facilitate reproducible analysis of neuroimaging data. Analysis pipelines for neuroimaging data typically rely on specific versions of packages and software, and are dependent on their native operating system. These dependencies mean that a working analysis pipeline may fail or produce different results on a new computer, or even on the same computer after a software update. Neurodesk provides a platform in which anyone, anywhere, using any computer can reproduce your original research findings given the original data and analysis code.