Jupyter for high-performance and parallel computing, for example, at supercomputing centers and scientific facilities.
Imagine querying 420+ billion rows of astronomical catalog data — spanning 30 major sky surveys, observed over decades with telescopes on three continents — from a Jupyter notebook in your browser in seconds. No download. No HPC allocation request. No waiting.
Updates on IPython Parallel; new features, future direction
Delivering interactive computing to universities at a national scale with a Jupyter stack.
Jupyter is the “Google Docs” of data science. It provides that same kind of easy-to-use ecosystem, but for interactive data exploration, modeling, and analysis.
We are excited to share more news about the Jupyter Community Workshop for Scientific User Facilities and High-Performance Computing!
