During this hands-on workshop, we will introduce parallel and distributed computing in MATLAB with a focus on speeding up application codes and offloading compute. By working through common scenarios and workflows using hands-on demos, you will gain a detailed understanding of the parallel constructs in MATLAB, their capabilities, and some of the common hurdles that you’ll encounter when using them.

Highlights

• Multithreading vs multiprocessing
• When to use parfor vs parfeval constructs
• Creating data queues for data transfer
• Leveraging NVIDIA GPUs
• Parallelizing Simulink models
• Working with large data

This sessions is a collaboration with Tufts University and the in-person session will be held at the Tufts University Data Lab, Tisch Library Room 203, 35 Professor’s Row, Medford, Ma.

Building a great interactive web map involves a lot more than just ‘printing’ out a JPG or PDF from ArcGIS Pro. A few critical questions include: How will your labels and symbology change at different scales? What details are in your pop-ups and how are they styled? Should you include interactive charts or graphs? Working in ArcGIS Online, users will better understand basic concepts of how to improve their interactive maps, learn how to build customizable web mapping applications using ArcGIS Instant Apps, and work on dynamic and interactive ArcGIS Dashboards.

This tutorial will introduce Boston University’s Shared Computing Cluster (SCC) in Holyoke, MA. This Linux cluster has more than 28000 processors and over 14 petabytes of storage available for Research Computing by students and faculty on the Charles River and BUMC campuses. A very large number of software packages for programming, mathematics, data analysis, plotting, statistics, visualization, and domain-specific disciplines are available as well on the SCC. You will get a general overview of the SCC and the facility that houses it and then a hands-on introduction covering connecting to and using the SCC for new users. This tutorial will cover a few basic Linux commands but we strongly encourage people to also take our more extensive “Introduction to Linux” tutorial. There will also be ample time for questions of all types about the SCC. For those in the BU community interested in using a particular package on the SCC, after taking this tutorial we also recommend viewing one of our short videos on that package if one is available.   Please read and follow these instructions prior to attending the tutorial.

This tutorial will give attendees a hands-on introduction to Linux. Topics covered will include a short history of Linux, logging in with ssh, the Bash shell and shell scripts, I/O redirection (pipes), file system navigation, and job control. Time permitting, attendees will edit, compile, and run a simple C program. If you have not connected to the SCC from your laptop before, please read and follow these instructions prior to attending the tutorial.

This tutorial will provide some more advanced techniques and common strategies used for interacting with the Shared Computing Cluster and its resources. The topics discussed during the tutorial include:

•    Customizing your environment
•    Parallel computing on the SCC
•    Jobs monitoring (CPU and memory usage)
•    Profiling programs for performance optimization
•    General optimization strategies

Prerequisites: some prior experience with high performance computing or attendance of our “Introduction to BU’s Shared Computing Cluster” tutorial.

This is an introduction to the essential features of Python. This first part of the tutorial includes an introduction to basic types, if-statements, functions, lists, dictionaries, loops, and modules. The tutorial includes the use of a popular Python development environment and covers setting up Python on your own computer in addition to using Python on the SCC. This is a two-part tutorial so please remember to sign up for both sessions. Recommended but not required: some programming experience. For example, you should understand concepts like loops and functions. If you do not have Python installed on your home machine, please read and follow these instructions prior to attending the tutorial.

This tutorial is a continuation of “Introduction to Python, Part One” and introduces more features of the language, common libraries such as numpy and matplotlib, and the basics of debugging Python programs. Please make sure you sign up for part one as well. If you do not have Python installed on your home machine, please read and follow these instructions prior to attending the tutorial.

Human language/communication can be studied computationally through Natural Language Processing (NLP). We’ll explore the basics of NLP using Python and PyTorch; no prior machine learning experience necessary, basic Python knowledge is helpful but not necessary. We will look at the bigram character model and build statistical and neural network implementations. The core concepts we will examine are applicable beyond our introductory example to neural networks/NLP in general.

Julia is a high-performance programming language, but with many features more common to lower performance interpreted languages. Many of its features are well suited for numerical analysis and computational science, with functions and syntax that are built around supporting this. This tutorial presents an introduction via solving hands-on example problems; this motivates the syntax/tools in a “why” versus “what” way. The tutorial introduces participants to common ways of using Julia and basic features including operators, loops, conditionals, and functions.

Python is now widely used for numerical calculations and data analysis. This tutorial is an introduction primarily to the Numpy library which provides data structures and algorithms that are optimized for numeric data. The Numpy library is the basis for a wide variety of numeric and graphics libraries in Python. The usage of the numpy multi-dimensional array type will be covered in detail. The Scipy library and how it can be effectively used with Numpy arrays and other Python data structures will be discussed. This tutorial assumes familiarity with Python. Prerequisite: If you are new to the Python programming language we strongly recommend that you also register for the “Introduction to Python” two-part tutorial. If you do not have Python installed on your home machine, please read and follow these instructions prior to attending the tutorial.

This tutorial will introduce the basics of Data Analysis with Python and its powerful libraries such as Pandas and Matplotlib.

•    Importing and Exporting the data
•    Basic data processing, cleaning, and manipulation
•    Basic inferential statistical analysis
•    Data Visualization techniques

If you do not have Python installed on your home machine, please read and follow these instructions prior to attending the tutorial.

MATLAB is an interpreted programming language. It was originally developed for linear algebra and engineering problems, but now has wide applicability and toolboxes for areas ranging from medicine, economics, and machine learning. This tutorial presents an introduction via solving hands-on example problems; this motivates the syntax/tools in a “why” versus “what” way. Part One introduces participants to the user-interface and basic features including operators, loops, and conditionals. Please also register for Part Two. No prior programming experience in any language is required to attend this course. If you do not have MATLAB installed on your home machine, please read and follow these instructions prior to attending the tutorial.

MATLAB is an interpreted programming language. It was originally developed for linear algebra and engineering problems, but now has wide applicability and toolboxes for areas ranging from medicine, economics, and machine learning. This tutorial presents an introduction via solving hands-on example problems; this motivates the syntax/tools in a “why” versus “what” way. Part Two introduces participants to basic features including file reading/writing, functions, and text processing. Please also register for Part One. No prior programming experience in any language is required to attend this course.

R is the most powerful, rapidly developing, highly reliable, open source statistical language. It is widely used among statisticians for the development of statistical software and for data analysis. New features appear every few months.

•    operators and arithmetic operations
•    atomic types, variable rules and built-in constants
•    scalar and vector function overview
•    working with data (workspace setup as well as reading, creating, exploring, and saving data)
•    working with R data types (vectors, matrices, lists, data frames)
•    working with script files
•    installing and loading R extension packages and getting help
•    overview of functions for data analysis

•    know the basics of the R environment.
•    get a solid understanding of various data types and objects used in R.
•    be able to create, load and analyze data.
•    find appropriate functions and get necessary help and examples for these functions.

If you do not have R and RStudio installed on your home machine, please read and follow these instructions prior to attending the tutorial.

“Tidy data” is a term that describes a standardized approach to structuring datasets to make statistical analyses and visualizations easier. In this tutorial you will learn how to modify, filter, arrange, and summarize your data with dplyr and other tidyverse packages. We will go over operations like merging two or more datasets, reshaping your data into the layout that works the best, and summarizing the data to explore hidden levels of information. Prerequisite: If you are new to the R environment we strongly recommend that you also register for the “Introduction to R” tutorial. If you do not have R and RStudio installed on your home machine, please read and follow these instructions prior to attending the tutorial.

The R package ggplot2 is a reliable and powerful tool for graphics and plotting scientific data. This tutorial will cover the theory behind ggplot2’s approach to visualization. We will cover the general flow of building a plot, mapping aesthetics, adding layers, and manipulating scales, facets, and coordinates. After this tutorial, you will be able to navigate the ggplot2 package with an understanding of how to design visualizations from data frames versus vectors (as in base R graphics) for elegant and professional illustrations of data. Documentation and alternative resources are included to help you continue developing in ggplot2 on your own after the tutorial. Prerequisite: If you are new to the R environment we strongly recommend that you also register for the “Introduction to R” tutorial. If you do not have R and RStudio installed on your home machine, please read and follow these instructions prior to attending the tutorial.

This tutorial is the third in a series of R tutorials. It introduces basic R programming. It covers the following topics:

•    if-else statements
•    loops
•    user functions and argument definitions
•    local and global variables
•    apply function family
•    sourcing

Prerequisite: We strongly recommend that you also register for the “Introduction to R” tutorial if you are new to the R environment. If you do not have R and RStudio installed on your home machine, please read and follow these instructions prior to attending the tutorial.

This tutorial will introduce select core Geographic Information System (GIS) theory concepts that are utilized by the majority of GIS software and GIS libraries. The goal of this tutorial is to get you familiar with common GIS terminology and concepts that may not be clearly described when reading “How To” manuals of GIS software packages and GIS libraries. Topics that will be covered include:

•    What is GIS?
•    Geographic Coordinate Systems & Projections
•    Spatial Data Models
•    Data Layers
•    Overview of spatial data files
•    Example of a GIS workflow
•    Overview of available GIS software and libraries

The content will be presented in lecture style and therefore no software needs to be installed prior to the tutorial.

QGIS is an open source GIS desktop application that can be downloaded for free and it provides a collection of tools for managing, analyzing, and visualizing spatial data. This application has a resemblance to commercial GIS products ArcGIS Pro and ArcMap. In this session I will introduce you to the user interface of QGIS and go through simple workflows to get you started on using the software, such as importing data, symbolizing data, adding labels, and more. Prerequisite: Introduction to GIS Theory Related Tutorials:

•    If you plan to use the SCC for your GIS needs, I recommend taking “Introduction to BU’s Shared Computing Cluster”.

If you do not have QGIS installed, please read and follow these instructions prior to attending the tutorial.

The ArcGIS Online Web Portal is the starting point for any ArcGIS Online projects. It is a cloud based web application that allows you to upload and store your own GIS data, save web maps, StoryMaps, and other ArcGIS Online applications in a centralized location. You can control access permissions for your content and share completed work with the world, or restrict access to only your working group. It also provides tools to search GIS data that was published on the ArcGIS Online platform and makes it easy to import that data into your applications. In this tutorial, I will give a brief introduction on what is ArcGIS Online. Then we will log onto the web portal and I will explain the structure of the user interface and how to use the basic tools to manage your content, manage permissions, and search for data. In this tutorial we will not cover how to use specific applications (such as Web Viewer, StoryMaps, Dashboards, etc). No software installation is required; just an internet connection and an internet browser. You will need an ArcGIS Online account.