Parallel and distributed computing is a great solution in the modern systems world. The important issue when teaching parallel and distributed computing is to prepare students for challenges related to hardware architectures and interconnections, methods for software construction, and mathematical models.

The core idea is a unique framework of two key components that unite open-source communities. The first is JupyterHub (which has recently matured to the new JupyterLab), a multiuser Jupyter Notebook server deployed as a platform that allows users to concurrently use their Jupyter Notebooks, create and execute code, and perform exploratory data analysis. The Jupyter Notebook, an open-source web application for creating and sharing live code documents with visualization opportunities, enables the use of tools, including virtual terminals, image viewers, and programming editors, on the unique web interface of the multiuser version. The second component, also from the open-source community, is the interpreted high-level programming language Python. It is the basis of Project Jupyter, which is devoted to promoting and developing an open-source ecosystem for interactive computing regardless of programming languages in use.

I am impressed with the paper’s presentation and useful suggestions, and it is helpful reading for anyone who requires an advanced research computing infrastructure. Its visual presentation includes diagrams of a unifying computing environment and JupyterHub’s deployment, and also the roles and interactions of each component. The authors describe the course modules deployed through the unifying framework. Each module has essential information on the content and assignments. The authors also describe module updating through the open-source GitHub repository platform.