seeMqv - Scalable Multivariate Query Driven Visualization for Large Multivariate Volume Data
Volumetric datasets with multiple variables on each voxel over multiple timesteps are often complex, especially when considering the exponentially large attribute space formed by the variables, and spatial and temporal dimensions. It is intuitive, practical, and thus often desirable, to interactively select a subset of the data from within that high-dimensional value space for efficient visualization. This approach is straightforward to implement if the dataset is small enough to be stored entirely in-core. However, to handle datasets sized at hundreds of gigabytes and beyond, this simplistic approach becomes infeasible. More sophisticated solutions are needed. In this work, we developed a system that supports efficient visualization of an arbitrary subset, selected by range-queries, of a large multivariate time-varying dataset. By employing specialized data structure and schemes of data distribution, our system can leverage a large number of networked computers as parallel data servers and guarantees a near optimal load-balance. We demonstrate our system of scalable data servers using a large dataset from a supernova simulation.
In the above photograph of the Everest visualization facility at the Oak Ridge National Laboratory, we demonstrate an on-demand visualization environment for hypotheses-driven scientific research, supported by an installation of our scalable data servers.
Standing in front of Everest are Sean Ahern, the taskforce lead of visualization at ORNL, and Colin Molenhour.
The TSI data was provided by John Blondin and Anthony Mezzacappa under the auspices of the DOE SciDAC Terascale Supernova Initiative.
'Scalable Data Servers for Large Multivariate Volume Visualization, Markus Glatter, Colin Mollenhour, Jian Huang and Jinzhu Gao, IEEE Transactions on Visualization and Computer Graphics, 12(5), pp. 1291-1299, 2006. (special issue for IEEE Visualization’06)