Writing, Running, and Analyzing Large-scale Scientific Simulations with Jupyter Notebooks

Abstract

Large-scale scientific simulations typically output massive amounts of data that must be later read in for post-hoc visualization and analysis. With codes simulating complex phenomena at ever-increasing fidelity, writing data to disk during this traditional high-performance computing workflow has become a significant bottleneck. In situ workflows offer a solution to this bottleneck, whereby data is simultaneously produced and analyzed without involving disk storage. In situ analysis can increase efficiency for domain scientists who are exploring a data set or fine-tuning visualization and analysis parameters. Our work seeks to enable researchers to easily create and interactively analyze large-scale simulations through the use of Jupyter Notebooks without requiring application developers to explicitly integrate in situ libraries.