veyepar: UV-CDAT Resharable Analyses and Diagnostics

Author(s):	Charles Doutriaux
Location	A2
Date	mar Tue 19	Days Raw Files
Start	11:05	First Raw Start	10:59
Duration	00:50:00	Offset	0:05:59
End	11:55	Last Raw End	13:16
Chapters	00:00 0:00:35 0:21:04
Total cuts_time	27 min.

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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

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