Extreme Scaling of Production Visualization Software on Diverse Architectures

Authors:
Hank Childs;David Pugmire;Sean Ahern;Brad Whitlock;Mark Howison; Prabhat;Gunther H. Weber;E. Wes Bethel
Affiliations:
Lawrence Berkeley National Laboratory;Oak Ridge National Laboratory;Oak Ridge National Laboratory;Lawrence Livermore National Laboratory;Lawrence Berkeley National Laboratory;Lawrence Berkeley National Laboratory;Lawrence Berkeley National Laboratory;Lawrence Berkeley National Laboratory
Venue:
IEEE Computer Graphics and Applications
Year:
2010

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Abstract

A series of experiments studied how visualization software scales to massive data sets. Although several paradigms exist for processing large data, the experiments focused on pure parallelism, the dominant approach for production software. The experiments used multiple visualization algorithms and ran on multiple architectures. They focused on massive-scale processing (16,000 or more cores and one trillion or more cells) and weak scaling. These experiments employed the largest data set sizes published to date in the visualization literature. The findings on scaling characteristics and bottlenecks will help researchers understand how pure parallelism performs at high levels of concurrency with very large data sets.