Visualization at Extreme Scale Concurrency
Hank Childs, David Pugmire, Sean D. Ahern, Brad Joseph Whitlock, Mark Howison, Gunther H. Weber, E. Wes Bethel · Chapman & Hall/CRC computational science series/Chapman & Hall/CRC computational science · 2012
There are some important and motivating questions that drive the research for processing massive data sets, like will it be possible to use the simpler pure parallelism technique to process tomorrow's data?Can pure parallelism scale sufficiently to process massive data sets?To answer these questions, the researchers performed a series of experiments, originally published in IEEE Computer Graphics and Applications [2] and forming the basis of this report, that studied the scalability of pure parallelism in visualization software on massive data sets.These experiments utilized multiple visualization algorithms and were run on multiple architectures.There were two types of experiments performed.The first experiment examined performance at a massive scale: 16,000 or more cores and one trillion or more cells.The second experiment studied whether the approach can maintain a fixed amount of time to complete an operation when the data size is doubled and the amount of resources is doubled, also known as weak scalability.At the time of their original publication, these experiments represented the largest data set sizes ever published in visualization literature.Further, their findings still continue to contribute to the understanding of today's dominant processing paradigm (pure parallelism) on tomorrow's data, in the form of scaling characteristics and bottlenecks at high levels of concurrency and with very large data sets.