Extreme scaling of production visualization software on diverse architectures.
This article presents the results of experiments studying how the pure-parallelism paradigm scales to massive data sets, including 16,000 or more cores on trillion-cell meshes, the largest data sets published to date in the visualization literature. The findings on scaling characteristics and bottlenecks contribute to understanding how pure parallelism will perform in the future.
Childs
Hank
H
Lawrence Berkeley National Laboratory, and University of California at Davis, CA, USA. hchilds@lbl.gov
Pugmire
David
D
Ahern
Sean
S
Whitlock
Brad
B
Howison
Mark
M
Weber
Gunther H
GH
Bethel
E Wes
EW