An Introduction to Scientific Software Tools & Parallel Algorithms (SSPA)¶

Item	Link
Jupyter Book Status
Rendered book	https://luca-heltai.github.io/sspa/
Course repository	https://github.com/luca-heltai/sspa
Author	Luca Heltai

Course materials for a 30-hour PhD-level class (10 × 3h sessions) on practical tools for scientific software and introductory parallel algorithms.

Overview¶

This course provides a comprehensive, hands-on introduction to the tools and workflows used in modern scientific software development and an applied introduction to parallel algorithms and performance analysis. Topics include:

• Unix shell and HPC usage (ssh, tmux, Slurm job scheduler)

• Version control using Git (local and collaborative workflows)

• Automated documentation (Doxygen for C/C++, Sphinx/MyST for Python and general docs)

• Unit and functional testing (Google Test for C++, pytest for Python)

• Containerization and reproducibility (Docker, Apptainer/Singularity)

• Continuous Integration and automation (GitHub Actions)

• Basics of parallel computing: speedup, efficiency, Amdahl’s and Gustafson’s laws

Learning outcomes¶

By the end of the course, students will be able to:

• Operate comfortably in a Linux command-line environment and write basic Bash scripts.

• Submit and monitor jobs on a Slurm-based cluster (we provide a Dockerized simulator for exercises).

• Use Git to manage projects, branch, merge, resolve conflicts, and collaborate via pull requests.

• Produce documentation with Doxygen and Sphinx/MyST, including API references and tutorial pages.

• Write unit and functional tests for C++ and Python code and integrate them into CI.

• Build and run Docker and Apptainer containers to package software environments for reproducibility.

• Set up simple CI workflows (GitHub Actions) to run tests and build artifacts automatically.

• Measure simple parallel programs, compute speedup and efficiency, and interpret results using Amdahl’s and Gustafson’s laws.

Quick start¶

1. Browse the compact course book in jupyterbook/ (index and lectures/).

2. To build the book locally (requires jupyter-book):

pip install -U jupyter-book
jupyter-book build jupyterbook

1. Hands-on examples commonly use Docker. See docker-images/ for Dockerfiles and usage notes if present.

License¶

Content: CC-BY. Code examples: MIT. See LICENSE for details.

Course repository structure¶

Typical layout (some folders may be added later):

• jupyterbook/ — the course book (index, TOC, lectures)

• jupyterbook/lectures/ and jupyterbook/slides/ — session notes and slides

• codes/ — example programs used in lectures

• exercises/ — assignment descriptions and starter files

• docker-images/ — Dockerfiles and compose files for the mini-cluster

• .github/workflows/ — example CI pipelines used to build/test the material

Use the table of contents to navigate the 10 sessions (each ~3h). See the top-level repository README for quick setup instructions.

Below is a quick summary of the course topics collected in this Jupyter Book:

• Unix shell and HPC environments (Slurm)

• Version control with Git

• Documentation (Doxygen, Sphinx)

• Testing (gtest, pytest)

• Containerization (Docker, Apptainer)

• Continuous Integration (GitHub Actions)

• Parallel algorithms and performance analysis

Use the table of contents to navigate the 10 sessions (each ~3h).