Title
Parncl And Pargal: Data-Parallel Tools For Postprocessing Of Large-Scale Earth Science Data
Abstract
Earth science high-performance applications often require extensive analysis of their output in order to complete the scientific goals or produce a visual image or animation. Often this analysis cannot be done in situ because it requires calculating time-series statistics from state sampled over the entire length of the run or analyzing the relationship between similar time series from previous simulations or observations. Many of the tools used for this postprocessing are not themselves high-performance applications, but the new Parallel Gridded Analysis Library (ParGAL) provides high-performance data-parallel versions of several common analysis algorithms for data from a structured or unstructured grid simulation. The library builds on several scalable systems, including the Mesh Oriented DataBase (MOAB), a library for representing mesh data that supports structured, unstructured finite element, and polyhedral grids; the Parallel-NetCDF (PNetCDF) library; and Intrepid, an extensible library for computing operators (such as gradient, curl, and divergence) acting on discretized fields. We have used ParGAL to implement a parallel version of the NCAR Command Language (NCL) a scripting language widely used in the climate community for analysis and visualization. The data-parallel algorithms in ParGAL/ParNCL are both higher performing and more flexible than their serial counterparts.
Year
DOI
Venue
2013
10.1016/j.procs.2013.05.291
2013 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE
Keywords
Field
DocType
data analysis, data parallelism, postprocessing
Data mining,Computer science,Unstructured grid,Computational science,Artificial intelligence,Operator (computer programming),Visualization,Earth science,Data parallelism,Animation,NCAR Command Language,Machine learning,Scripting language,Scalability
Conference
Volume
ISSN
Citations 
18
1877-0509
3
PageRank 
References 
Authors
0.45
10
14
Name
Order
Citations
PageRank
Robert L. Jacob114013.23
Jayesh Krishna2302.71
Xiabing Xu3654.65
Tim Tautges451.49
Iulian Grindeanu582.71
Robert Latham630.45
Kara Peterson7164.78
Pavel B. Bochev838267.69
Mary Haley931.13
David Brown1030.79
Richard Brownrigg1131.13
Dennis G. Shea128818.76
W. Huang1326733.97
Don Middleton1412312.58