UV-CDAT Re-sharable Analyses and Diagnostics (U-ReAD): a framework to create and share UV-CDAT plugins
Authors: Doutriaux, Charles, LLNL
Some of today's greatest challenges to the scientific community are'big data', 'reproducibility/transparency' and 'code sharing'.
The state-of-the-art Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) environment addresses the first two issues with new visualizations and techniques to address big data and provenance.
This talk addresses code re-sharing and re-distribution by introducing the UV-CDAT Re-sharable Analyses and Diagnoses (U-ReAD). U-ReAD will offer scientists a complete set of tools (framework) based on the Python programming language along with a code repository. U-ReAD's goal is to use structured documentation to help build the interface between UV-CDAT and a diagnostic, with few or no changes to the original code. This framework will allow scientists to quickly and seamlessly re-implement their diagnostics so that they will fit perfectly into the UV-CDAT environment. As a result U-ReAD-enhanced diagnostics will be automatically provenance-enabled, making it easy to reproduce any set of results exactly and transparently, a crucial functionality considering today's increased scrutiny toward scientific results.
This talk aims to demonstrate how easy it can be to plug any diagnostic into UV-CDAT using U-ReAD. We will show how few changes are necessary to create these plugins and how 'augmented' the diagnostics are in return.
U-ReAD's developers also hope to create a central repository of U-ReAD-enhanced tools so that scientists can easily share their tools. This talk will show what is in store along these lines. http://u-read.llnl.gov