OpenStack Tutorial For Beginners
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OpenStack Tutorial For Beginners
CaRCC Data Facing Track
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The Data-Facing Track of the People Network brings together people from research computing groups, libraries, research institutes, and other organizations who support data-enabled research. Many of us are also Researcher-Facing, but this track is an opportunity to discuss the varied challenges of working with data.
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.
HPCwire
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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.
Feed Forward NNs and Gradient Descent
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Feed-forward neural networks are a simple type of network that simply rely on data to be "fed-forward" through a series of layers that makes decisions on how to categorize datum. Gradient descent is a type of optimization tool that is often used to train machines. These two areas in ML are good starting points and are the easiest types of neural network/optimization to understand.
Machine Learning with sci-kit learn
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In the realm of Python-based machine learning, Scikit-Learn stands out as one of the most powerful and versatile tools available. This introductory post serves as a gateway to understanding Scikit-Learn through explanations of introductory ML concepts along with implementations examples in Python.
Python Data and Viz Training (CCEP Program)
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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!
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.
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.
Fairness and Machine Learning
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The "Fairness and Machine Learning" book offers a rigorous exploration of fairness in ML and is suitable for researchers, practitioners, and anyone interested in understanding the complexities and implications of fairness in machine learning.
Federated CI Resources
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Discussion about contributing cycles to the Open Science Grid.
Ask.CI Q&A Platform for Research Computing
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Bridges-2 Home Page
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Landing Page for Bridges-2 information
Paraview UArizona HPC links (beginner)
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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.
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.
Jetstream Home
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Jetstream2 makes cutting-edge high-performance computing and software easy to use for your research regardless of your project’s scale—even if you have limited experience with supercomputing systems.Cloud-based and on-demand, the 24/7 system includes discipline-specific apps. You can even create virtual machines that look and feel like your lab workstation or home machine, with thousands of times the computing power.
Slurm User Group Mailing List
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Probabilistic Semantic Data Association for Collaborative Human-Robot Sensing
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Humans cannot always be treated as oracles for collaborative sensing. Robots thus need to maintain beliefs over unknown world states when receiving semantic data from humans, as well as account for possible discrepancies between human-provided data and these beliefs. To this end, this paper introduces the problem of semantic data association (SDA) in relation to conventional data association problems for sensor fusion. It then, develops a novel probabilistic semantic data association (PSDA) algorithm to rigorously address SDA in general settings. Simulations of a multi-object search task show that PSDA enables robust collaborative state estimation under a wide range of conditions.
High performance computing 101
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An introductory guide to High Performance Computing.
ConnectCI
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Connect.Cybinfrastructure is a family of portals, each representing a program that is serving a segment of the research computing and data community. Each portal provides program-specific information, as well a custom "view" into a common database. The portal was originally developed to support project workflows and a knowledge base of self service learning resources for the Northeast Cyberteam. Subsequently, it was expanded to provide support to multiple cyberteams and other research computing communities of practice. We welcome additional communities, please contact us if you are interested in participating. Central to the Portal is an extensive and ever-evolving tagging infrastructure which informs every aspect of the Portal. The tag taxonomy was initially developed by the Northeast Cyberteam to categorize subject matter relevant to practitioners of Research Computing Facilitation and is ever changing due to the frequent introduction of new technology in domains that characterize the field of research computing.
Wiki for Onboarding onto the C3DDB Cluster at MGHPCC
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This is a resource for researchers and students looking to on-board onto the c3ddb cluster at MGHPCC. In the code section, there are example job submission scripts for the different queues on c3ddb.
GDAL Multi-threading
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Multi-threading guidance when using GDAL.
Introduction to MP
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Open Multi-Processing, is an API designed to simplify the integration of parallelism in software development, particularly for applications running on multi-core processors and shared-memory systems. It is an important resource as it goes over what openMP and ways to work with it. It is especially important because it provides a straightforward way to express parallelism in code through pragma directives, making it easier to create parallel regions, parallelize loops, and define critical sections. The key benefit of OpenMP lies in its ease of use, automatic thread management, and portability across various compilers and platforms. For app development, especially in the context of mobile or desktop applications, OpenMP can enhance performance by leveraging the capabilities of modern multi-core processors. By parallelizing computationally intensive tasks, such as image processing, data analysis, or simulations, apps can run faster and more efficiently, providing a smoother user experience and taking full advantage of the available hardware resources. OpenMP's scalability allows apps to adapt to different hardware configurations, making it a valuable tool for developers aiming to optimize their software for a range of devices and platforms.
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.