Conclusion

[ROUGH] Jupyter in HPC is now commonplace. We have been able to give hundreds of HPC users a rich user interface to HPC through Jupyter. In the supercomputing context, we look at Jupyter as a tool that will help make it easier for our users to take advantage of supercomputing hardware and software. Some of that will come from us at supercomputing centers. Jupyter as a project needs to not make design decisions that break things for us, or lock us into one way of doing things. Each HPC center is different and that means that for Jupyter to remain useful to HPC centers and supercomputing it needs to maintain its high level of abstraction. We should make this into a bulleted list of demands :)
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Jupyter is a flexible, popular literate-computing web application for creating documents called "notebooks" containing code, equations, visualization, and text. Notebooks contain both computer code and rich text elements (paragraphs, equations, figures, widgets, links). They are human-readable documents containing analysis descriptions and results but are also executable data analytics artifacts.
Supercomputing is about more than just high performance or scale. High-performance computing (HPC) is about composing and concentrating resources for computation, data storage, and networking so as to provide users with performance far beyond what they can obtain from a laptop or desktop computer. What distinguishes supercomputing from other forms of HPC is that supercomputers tend to be purpose-built to solve very large, very complex problems using hardware with unique capabilities and software designed to exploit those capabilities. [To make things concrete, give example of an application or library that takes advantage of some form of supercomputing hardware to solve some problem. Bonus points if it can resonate with Jupyter. Good luck!]