ICER Courses in Fall 2025 - HPC with Python and Reproducible Computational Workflows

The Institute for Cyber-Enabled Research, in partnership with the Department of Computational Mathematics, Science, and Engineering, will teach two graduate courses in the fall of 2025: "High-Performance Computing with Python" and "Reproducible Computational Workflows." The overarching goal of these courses is to provide you with practical tools and experiences that will help you to become a more productive and capable researcher. The course descriptions are below, and you can reach out to the instructors with any questions. 

High-Performance Computing with Python 
CMSE 890-601

Date, time, location, instructor: Friday 10:20 a.m. – 12:10 p.m. in 1455A, BPS, C. Kopenhafer

Prerequisites:

• Required: ICER’s Intro to Linux workshop (or equivalent experience)
• Required: Experience programming in Python (CMSE 201, CSE 231, CMSE 801, or equivalent experience)
• Familiarity with scientific Python packages (e.g. NumPy, SciPy, Pandas) recommended
• Experience using Git for version control is recommended

Contact Dr. Claire Kopenhafer (kopenhaf@msu.edu) with any questions.

Course description: Python already supports a wide range of packages that can assist in your research. Now, learn a variety of techniques for making your software more efficient, reducing the time it takes to get research results. 

Through this course, you will:

• Understand multiple approaches for improving Python performance
• Implement these approaches in your own Python code
• Understand the basic structure of high-performance computers (both at ICER and beyond)
• Synthesize software- and system-level understanding to effectively execute performant Python software

Students are encouraged to use their own research software as the basis for their class projects.

Reproducible Computational Workflows
CMSE 890-602

Date, time, location, instructor: Friday 12:40 p.m. - 2:30 p.m. in 1455A, BPS, A. Fullard

Prerequisites:

• Required: Intro to Linux ICER workshop (or equivalent experience).
• Required: Basic knowledge of a programming language such as Python or R.

Contact Dr. Andrew Fullard (fullarda@msu.edu) with any questions.

Course description: What can a reproducible computational research workflow do for you?

• Automation can make your research faster!
• Research is a collaborative process. Sharing data and processes in research teams is often difficult and slow, but it doesn’t have to be.
• It will make it easier to reproduce your research.
• It will help you manage your data.
• You can easily keep track of changes to your data and processes.

This course will teach you the basics of data analysis workflows, from planning through to creation. You will understand modularity, automation, data structures, version control, and code design for research data and software. The course will not teach you how to work with low-level languages such as Fortran or C++ to develop simulations.

By the end of the course, you will have produced a useful data analysis workflow that you can immediately apply to your research!