Extreme Scaling of Production Visualization Software on Diverse Architectures
Hank Childs, David Pugmire, Sean D. Ahern, Brad Joseph Whitlock, Mark Howison, Prabhat, Gunther H. Weber, E. Wes Bethel · IEEE Computer Graphics and Applications · 2010
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.