Becker Library has partnered with the Center for High Performance Computing (CHPC) and the Institute for Informatics (I2) to offer free trainings to the Washington University Medical Center community on a variety of research computing topics, including:
- Computing 101
These workshops are meant to serve as an introduction to research computing basics and prepare participants for ongoing learning. Check below to see what’s coming up or sign up for the mailing list to be notified each time a new workshop is scheduled.
There are no workshops scheduled at this time. Check back later for upcoming workshops or use the link above to sign up for the mailing list.
These workshops were geared toward researchers new to high performance computing and the command line interface. During each workshop session important background concepts related to computing were introduced in a simple presentation format, followed by hands-on exercises. While these workshops were focused on using the CHPC, the knowledge gained could be applied to other computing environments. The materials from these workshops can be found here.
#1 Tools to Connect and Move Data & Getting Around the Linux Command Line Interface – 3/26/19
Goals: to develop a basic understanding of the tools needed to connect to the CHPC and transfer data back and forth, and become familiar with the Unix file system and basic Unix commands
#2 Text Editors and Shell Scripts – 4/2/19
Goals: to gain knowledge of some of the options for text editors and learn how to write and execute basic shell scripts
#3 Running Jobs on the CHPC Cluster – 4/9/19
Goal: to learn how to write and submit batch scripts to run applications on the CHPC cluster
Two MATLAB workshops were led by a MathWorks Engineer on Thursday, May 3, 2018. See below for a description of each of the workshops and links to workshop materials.
Session #1: Image processing for medical applications using MATLAB
This MATLAB training seminar presentation included the following topics:
- Analyzing medical images using MATLAB Apps
- Image thresholding
- Finding ROI properties
- Automating your workflow with MATLAB
Workshop slides and other materials can be found here.
Session #2: Parallel Computing with MATLAB
This workshop was hands-on and focused on using MATLAB on the CHPC cluster. The following topics were covered:
- Getting started with parallel computations in MATLAB
- How well does your code scale?
- What’s taking so long? Profiling in parallel
The slides and hands-on activities from this workshop are posted on the CHPC MATLAB page.
These workshops were geared toward researchers new to programming and Python. Each workshop started with a 30 minute presentation covering important background information and then the remaining 90 minutes was devoted to hands-on activities.
Introduction to Python #1 – Getting Started with Python – 7/17/18 and 7/18/18
Goals: to develop a basic understanding of the Python programming language and to learn to run basic Python code in the interactive Python environment, Jupyter Notebook
Introduction to Python #2 – Using Python for Data Analysis – 7/24/18 and 7/25/18
Goals: to learn to write Python code to perform data analysis and visualization and to learn to run Python code in the virtual environment set up on the CHPC
These workshops were geared toward researchers new to programming and R. Each workshop started with a 30-minute presentation covering important background information and then the remaining 90 minutes were devoted to hands-on activities.
Introduction to R #1 – 9/6/18 and 9/7/18
Objectives: introduce R objects and classes, provide an overview of some popular data structures (vectors, matrices, and data frames), and touch on basic ways to manipulate data
Introduction to R #2 – 9/13/18 and 9/14/18
Objectives: introduce basic methods of data visualization using R base and ggplot2
Introduction to R #3 – 9/20/18 and 9/21/18
Objectives: introduce advanced visualizations using the ggplot2 package in R
Introduction to R #4 – 9/27/18 and 9/28/18
Objectives: introduce genomic data analysis using R/Bioconductor and associated visualizations